Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.

In this post, we’ve compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks. But before you dive into the deep end, you need to make sure you’ve got the fundamentals down pat.

It doesn’t matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats—linear algebra, calculus, optimization, probability—to get ahead.

Top machine learning books to read for beginners

1. Matrix Computations

   

   This 2013 edition by Golub and Van Loan, published by The Johns Hopkins University Press, teaches you about matrix analysis, linear systems, eigenvalues, discrete Poisson solvers, least squares, parallel LU, pseudospectra, Singular Value Decomposition, and much more.

   This book is an indispensable tool for engineers and computational scientists. It has great reviews on Amazon, especially by users looking for problems, discussions, codes, solutions, and references in numerical linear algebra.

   Free Book:Download here

2. A Probabilistic Theory of Pattern Recognition

   

   Written by Devroye, Lugosi, and Györfi, this an excellent book for graduate students and researchers. The book covers various probabilistic techniques including nearest neighbour rules, feature extraction, Vapnik-Chervonenkis theory, distance measures, parametric classification, and kernel rules.

   Amazon reviewers laud it for its nearly 500 problems and exercises.

   Wikipedia says “The terms pattern recognition, machine learning, data mining and knowledge discovery in databases are hard to separate, as they largely overlap in their scope.”

   No wonder, machine learning enthusiasts swear by this comprehensive, theoretical book on “nonparametric, distribution-free methodology in Pattern Recognition.”

   Free Book:Download here

3. Advanced Engineering Mathematics

   

   Erwin Kreyszig’s book beautifully covers the basics of applied math in a comprehensive and simplistic manner for engineers, computer scientists, mathematicians, and physicists.

   It teaches you Fourier analysis, vector analysis, linear algebra, optimization, graphs, complex analysis, and differential and partial differential equations.

   It has up-to-date and effective problem sets that ensure you understand the concepts clearly.

4. Probability and Statistics Cookbook

   A collection of math and stats reference material from the University of California (Berkeley) and other sources put together by Matthias Vallentin, this cookbook is a must-have for learners.

   There are no elaborate explanations but concise representations of key concepts. You can view it on GitHub, or download a PDF file using the link below.

   Free Book:Download here

5. An Introduction to Statistical Learning (with applications in R)

   

   This book written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is meant for non-math students.

   For data scientists, this is a valuable addition because of its R labs.

   The TOC includes linear regression, classification, resampling methods, linear model and regularization, tree-based methods, shrinkage approaches, clustering, support vector machines, and unsupervised learning.

   With interesting real-world examples and attractive graphics, this is a great text for statistical tools and techniques.

   Free Book:Download here

6. Probabilistic Programming and Bayesian Methods for Hackers

   

   Cameron Davidson-Pilon describes Bayesian methods and probabilistic programming from math and computation perspectives.

   The book discusses modeling Bayesian problems using Python’s PyMC, loss functions, the Law of Large Numbers, Markov Chain Monte Carlo, priors, and so lots more.

   The content is open sourced. The print version has updated examples, EOC questions, and improved and extra sections.

   Free Book:Download here

7. The Elements of Statistical Learning

   

   Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman (all three are Stanford professors) discuss supervised learning, linear methods of regression and classification, kernel smoothing methods, regularization, model selection and assessment, additive trees, SVM, neural networks, random forests, nearest neighbors, unsupervised learning, ensemble methods, and more.

   This book covers a broad range of topics is particularly useful for researchers interested in data mining and machine learning.

   You need to know linear algebra and some stats before you can appreciate the text.

   This is what one of the reviewers said about the book on Amazon: The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective.

   Free Book:Download here

8. Bayesian Reasoning and Machine Learning

   

   David Barber’s books is a comprehensive piece of writing on graphical models and machine learning.

   Meant for final-year undergraduate and graduate students, this text has ample guidelines, examples,and exercises. The author also offers a MATLAB toolbox and a related website.

   It covers inference in probabilistic models including belief networks, inference in trees,the junction tree algorithm, decision trees; learning in probabilistic models including Naive Bayes, hidden variables and missing data, supervised and unsupervised linear dimension reduction, Gaussian processes, and linear models; dynamic models including discrete- and continuous-state model Markov models, and distribution computation; and approximate inference.

   Free Book:Download here

9. Information Theory, Inference, and Learning Algorithms

   

   David MacKay exciting book discusses key concepts that form the core of machine learning, data mining, pattern recognition, bioinformatics, and cryptography.

   Amazon reviewers find the illustrations, depth, and “esoteric” approach remarkable.

   It is a great book on information theory and inference, which covers topics such as data compression, noisy-channel coding, probabilities, neural networks, and sparse graph codes.

   Free Book:Download here

10. Deep Learning

    

    This what Elon Musk, co-founder of Tesla Motors, has to say about this definitive text written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”

    The authors talk about applied math and machine learning basics, deep networks and modern practices, and deep learning research.

    For engineers interested in neural networks, this could well be their bible.

    The book is highly recommended for people in academia, providing the required mathematical background to fully appreciate deep learning in its current state.

    Free Book:Download here

11. Neural Networks and Deep Learning

    

    Michael Nielsen’s free online book is a comprehensive text on the core concepts of deep learning and artificial neural networks.

    The book has great interactive elements, but it does not provide solutions for the exercises. Laid out like a narrative, Nielsen holds onto core math and code to explain the key ideas.

    He talks about back propagation, hyper parameter optimization, activation functions, neural networks as functional approximates, regularization, a little about convolution neural networks, etc.

    The author includes valuable links to ongoing research and influential research papers and related tutorials.

    Free Book:Download here

12. Supervised Sequence Labelling with Recurrent Neural Networks

    

    Alex Graves discusses how to classify and transcribe sequential data, which is important in part-of-speech tagging, gesture, handwriting, and speech recognition, and protein secondary structure prediction.

    He talks about the role of recurrent neural networks in sequence labeling.

    Long short-term memory, a comparison of network architectures, hidden Markov model hybrids, connectionist temporal classification, multidimensional networks, and hierarchical sub sampling networks are other chapters in this book.

    Free Book:Download here

13. Reinforcement Learning: An Introduction

    

    Richard S. Sutton and Andrew G. Barto’s pioneering book onreinforcement learning covers the intellectual background, applications, algorithms, and the future of this exciting field. These University of Massachusetts Professors describe this artificial intelligence concept with clarity and simplicity.

    This book includes interesting topics such as Markov decision processes, Monte Carlo methods, dynamic programming, temporal-difference learning, eligibility traces, and artificial neural networks.

    Free Book:Download here

Summary

What’s better than getting educational resources that are free and authored by pioneers in the field?

Can’t think of a downside really…Especially for struggling students, these ebooks are a boon.

They don’t need to wait for the books to turn up at the library or swap with others;grab them and start learning!

So, what’s stopping you from picking up one of these excellent books and fashioning a successful career in data science, AI, or machine learning?

Variations of the Naive Bayes algorithm

1. Gaussian: The Gaussian Naive Bayes algorithm assumes distribution of features to be Gaussian or normal, i.e.,
   [latex]\displaystyle P(x_j|C_i)=\frac{1}{\sqrt{2\pi\sigma_{C_i}^2}}\exp{\left(-\frac{(x_j-\mu_{C_j})^2}{2\sigma_{C_i}^2}\right)}[/latex]
   Read more about it here.
2. Multinomial: The Multinomial Naive Bayes algorithm is used when the data is distributed multinomially, i.e., multiple occurrences matter a lot. You can read more here.
3. Bernoulli: The Bernoulli algorithm is used when the features in the data set are binary-valued. It is helpful in spam filtration and adult content detection techniques. For more details, click here.

Pros and Cons of Naive Bayes algorithm

Pros

• It is a relatively easy algorithm to build and understand.
• It is faster to predict classes using this algorithm than many other classification algorithms.
• It can be easily trained using a small data set.

Cons

• If a given class and a feature have 0 frequency, then the conditional probability estimate for that category will come out as 0. This problem is known as the "Zero Conditional Probability Problem." This is a problem because it wipes out all the information in other probabilities too. There are several sample correction techniques to fix this problem such as "Laplacian Correction."
• Another disadvantage is the very strong assumption of independence class features that it makes. It is near to impossible to find such data sets in real life.

Naive Bayes with Python and R

R Code

library(e1071)

library(caTools)

naiveBayes(formula, data, laplace = 0, subset, na.action = na.pass)

• formula: The traditional formula [latex]Y\sim X_1+X_2+\ldots+X_n[/latex]
• data: The data frame containing numeric or factor variables
• laplace: Provides a smoothing effect
• subset: Helps in using only a selection subset of the data based on some Boolean filter
• na.action: Helps in determining what is to be done when a missing value in the data set is encountered

> library(e1071)

> library(caTools)



> data(iris)



> iris$spl=sample.split(iris,SplitRatio=0.7)

# By using the sample.split() we are creating a vector with values TRUE and FALSE and by setting

  the SplitRatio to 0.7, we are splitting the original Iris dataset of 150 rows to 70% training

  and 30% testing data. 

> train=subset(iris, iris$spl==TRUE)#the subset of iris dataset for which spl==TRUE

> test=subset(iris, iris$spl==FALSE)



> nB_model <- naiveBayes(train[,1:4], train[,5]) 



> table(predict(nB_model, test[,-5]), test[,5]) #returns the confusion matrix

                setosa versicolor virginica

  setosa         17          0         0

  versicolor      0         17         2

  virginica       0          0        14

Python Code

>>> from sklearn.naive_bayes import GaussianNB

>>> from sklearn.naive_bayes import MultinomialNB

>>> from sklearn import datasets

>>> from sklearn.metrics import confusion_matrix

>>> from sklearn.model_selection import train_test_split



>>> iris = datasets.load_iris()

>>> X = iris.data

>>> y = iris.target



# Split the data into a training set and a test set

>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

>>> gnb = GaussianNB()

>>> mnb = MultinomialNB()



>>> y_pred_gnb = gnb.fit(X_train, y_train).predict(X_test)

>>> cnf_matrix_gnb = confusion_matrix(y_test, y_pred_gnb)



>>> print(cnf_matrix_gnb)

[[16 0 0]

 [ 0 18 0]

 [ 0 0 11]]



>>> y_pred_mnb = mnb.fit(X_train, y_train).predict(X_test)

>>> cnf_matrix_mnb = confusion_matrix(y_test, y_pred_mnb)



>>> print(cnf_matrix_mnb)

[[16 0 0]

 [ 0 0 18]

 [ 0 0 11]]

Applications

1. Text classification: It is used as a probabilistic learning method for text classification. The Naive Bayes classifier is one of the most successful known algorithms when it comes to the classification of text documents, i.e., whether a text document belongs to one or more categories (classes).
2. Spam filtration: It is an example of text classification. This has become a popular mechanism to distinguish spam email from legitimate email. Several modern email services implement Bayesian spam filtering.
   Many server-side email filters, such as DSPAM, SpamBayes, SpamAssassin, Bogofilter, and ASSP, use this technique.
3. Sentiment Analysis: It can be used to analyze the tone of tweets, comments, and reviews—whether they are negative, positive or neutral.
4. Recommendation System: The Naive Bayes algorithm in combination with collaborative filtering is used to build hybrid recommendation systems which help in predicting if a user would like a given resource or not.

Conclusion

In recent times, the Internet of Things is ubiquitous and is now a popular domain in the developer community. According to research by Statista, there are 6.21 million developers working in IoT and 5.36 million developers planning to work in IoT in next 6 months.

If you want to get started in IoT and are wondering which programming language to start with, here is a list of 11 popular programming languages used in IoT.

C

C, the language that was first developed to program telephone switches, is a reliable and reasonable choice for embedded system development. It is impressive because of its proximity to machine language.

It is a procedural language and the code is compiled and not interpreted. The code written in C is more reliable and scalable, and processor independence makes it a strong contender for IoT development. Because C is not platform independent, it enables IoT developers for code reuse, which can run on most of the systems.With the help of pointers, accessing and modifying addresses is easy in C.

C++

C++ is a middle-level programming language with imperative, object-oriented, and generic programming features with low-level memory manipulation.

C++ is designed with a bias toward system programming, embedded programming, resource-constrained devices and large systems. The design highlights of C++ are

• Performance
• Efficiency
• Flexibility of use

C++ is a popular choice among the embedded developers coding for Linux systems. Linux programming expertise, particularly with C++, is crucial for developing efficient, scalable, and secure IoT application. Here are a few features that make C++ a popular choice among IoT developers:

• Data hiding
• Stronger typing/ checking
• Multi-peripheral transparency using classes
• Templates (as always if used carefully)
• Initialisation lists

Go

Go is an open source programming language developed at Google. It combines the benefits of compiled language that is performance and security with that of aspeed of dynamic language.

It supports concurrent input, output, and processing on many different channels. Coordination of an entire fleet of sensors and actuators is possible when used correctly. The biggest risk is that the different channels don’t necessarily know about one another. If a programmer isn’t careful enough, a system could behave unpredictably because of a lack of coordination between channels.

In GO, gathering and sending data to various sensors and actuators is made easy by adding explicit hash table type.

The biggest advantage of GO is its ability to sort an entire network of sensors and making use of related IoT programming related devices. Go is now available on a wide variety of processors and platforms.

JavaScript

JavaScript is a scripting language with syntax similar to C. Initially, it was mainly used to create web pages, but now JavaScript is widely used in web servers, mobile apps, and IoT systems. JavaScript is good at event-driven applications; this allows every device to listen to various other events and respond to the concerned events. It has a garbage collector which eliminates freeing up of memory.

In your project, if you want to use the Apache server on a Raspberry Pi to collect data from a network of Arduino-based sensors, JavaScript would be good to start with.

Here are 10 reasons why JavaScript is used in IoT.

Python

The language, which was developed during a holiday break, went on to become the most preferred language for web development and started gaining popularity in embedded controls and IoT. Python is an interpreted language which can be either submitted for runtime compilation or run through one of theseveral pre-compilers so that compact executable code may be distributed.

The greatest benefit that Python offers to developers is readability with elegant syntax, without compromising on the size. Python’s clean syntax is apt for database arrangement. If your app requires data to be arranged in a database format and use tables for data control, Python is the best choice.

Python supports a huge number of libraries and modules, so you can get more stuff done with less code. It’s handy in more powerful edge devices, gateways, and also the cloud.

Rust

Rust is an open source, general-purpose, multi-paradigm, compiled programming language sponsored by Mozilla. Rust shares many of Go’s qualitiesand solves race condition problems of Go. Rust includes functions that eliminate race conditions for highly concurrent programs, making it a less risky language. Because of its ability to handle concurrent programming, Rust is now popular among IoT developers.

Rust is a safe, concurrent and practical language, supporting functional and imperative-procedural paradigms. It maintains these goals without a garbage collector. This makes Rust a useful language for the following use cases:

• Embedding in other languages
• Programming with specific space and time requirements
• Writing low-level code, like device drivers and operating systems

Rust is an improvement on current languages by having a number of compile-time safety checks which produce no runtime overhead and eliminates all data races.

Java

Java is an object-oriented language, and there are very few hardware dependencies built into the compiler, which makes it incredibly portable.

The biggest concern in IoT is security; with the Generic Connection Framework 8 (GCF 8), the Access Point API in Java provides the latest security standards and the highest levels of networked encryption and authentication which ensure data privacy.

All the object references in Java are implicit pointers which cannot be manipulated by application code. This automatically rules out the potential risk of memory access violations which can inevitably cause an application to stop all of a sudden.

Connectivity at the application level of the IoT system is also easily handled in Java with a comprehensive set of APIs, both standard and freely available through open source projects.

Assembly language

Assembly language is a low-level programming language and is specific to a particular type of computer architecture. In contrast, to other high-level programming languages, Assembly language is not portable across multiple architectures.

Assembly language is also called symbolic machine code. It is converted into executable machine code by a utility program.

Assembly language is best if you want to make your IoT project compact, minimal, and optimal.

ParaSail

ParaSail stands for Parallel Specification and Implementation Language. It is a compiled, object-oriented language with syntax similar to Java, Python, C#, and Ada.

ParaSail is a language that you must consider if you have a requirement for parallel processing in your IoT system.

ParaSail is capable of both specifying and implementing parallel applications. It supports both implicit and explicit parallelism. Every ParaSail expression is defined to have parallel evaluation semantics.

R

R is a programming language and environment for statistical computing and graphics. It is widely used by statisticians and data miners for building statistical software and data analysis.

Here are a few statistical and graphical techniques implemented by R and its libraries:

• Linear and nonlinear modeling
• Time-series analysis
• Classical statistical tests
• Clustering
• Classification and others

R is easily extensible through functions and extensions. It has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also made easy with its lexical scoping rules.

B#

B# was designed as a very small and efficient embedded control language. The embedded virtual machine (EVM) that allows B# to run on a variety of different hardware platforms only takes 24 kb of memory. B# is lean enough for 8-bit MCUs.

The syntax of B# looks a bit like C#, with support for the real-time control functions that are critical to make things happen in the real world. The B# code when coupled with a compact virtual machine could be easily ported and reused across multiple hardware platforms.

B# supports writing portable interrupt handlers and device addressing registers in a uniform way.

It also supports modern object-oriented features such as

• Namespaces
• Abstract and concrete classes
• Interfaces
• Delegates

In addition to these, B# caters to the embedded systems programmer with

• Efficient boxing/unboxing conversions
• Multi-threading statements
• Device addressing registers
• Deterministic memory defragmenter
• Field properties
• Interrupt handlers

Each of these features is directly supported by the constructs of B# and its underlying virtual machine. This helps to create, use, and reuse more portable and decoupled software components across different embedded systems applications.

If your project is going to live on embedded hardware platforms that aren’t as big and complex as a Raspberry Pi, then B# is the best option available.

Forth

Forth has been around since 1970 and is designed and optimized for embedded system programming. It is an imperative stack-based language and environment. It includes features such as

• Structured programming
• Concatenative programming
• Extensibility
• Reflection

Forth features both interactive execution of commands and the ability to compile sequences of commands for later execution.

Forth is used in the Open Firmware boot loader, in space applications such as spacecrafts, and other embedded systems where interaction with hardware is involved.

HiveQL

HiveQL is based on SQL but does not strictly follow the full SQL-92 standard. It runs on the Hadoop map-reduce framework but hides the complexities from the developers.

HiveQL does not offer extensions in SQL, including multi-table inserts and create table as select. It only offers basic support for indexes. Here are few things which HiveQL can do easily:

• Create and manage tables and partitions
• Evaluate functions
• Support various Relational, Arithmetic, and Logical Operators
• Download the contents of a table to a local directory or result of queries to HDFS directory

Pig Latin

Pig Latin is the language of Apache Pig, which is a high-level platform for creating programs that run on Apache Hadoop. Pig Latin can be extended using User Defined Functions (UDFs) which you can write in Java, Python, JavaScript, Ruby, or Groovy and then call directly from the language.

Here are a couple of key properties of Pig Latin:

• Ease of programming. With Pig Latin, it is easy to achieve parallel execution of simple and “embarrassingly parallel” data analysis tasks. Complex tasks with multiple interrelated data transformations are explicitly encoded as data flow sequences; this makes them easy to write, understand, and maintain.
• Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing you to focus on semantics rather than efficiency.

Extensibility. Users can create their own functions to do special-purpose processing.

Julia

Julia is a high-level, high-performance dynamic programming language for numerical analysis and computational science. Julia language provides

• A sophisticated compiler
• An extensive mathematical function library
• Numerical accuracy
• Distributed parallel execution

What makes Julia’s design unique is its type system with parametric polymorphism, and it types in a fully dynamic programming language. It also has multiple dispatch as its core programming paradigm. Julia allows concurrent, parallel, and distributed computing and direct calling of C and Fortran libraries without any glue code.

Julia’s LLVM-based JIT compiler combined with the language’s design allows it to often match the performance of C.

It does not impose any particular style of parallelism on the user. Instead, Julia provides flexibility to the user by providing a number of key building blocks for distributed computation through which it supports various styles of parallelism.

Introduction

Clustering algorithms are a part of unsupervised machine learning algorithms. As there is no target variable, the model is trained using input variables to discover intrinsic groups or clusters.

Because we don’t have labels for the data, these groups are formed based on similarity between data points. This tutorial covers clustering concepts, techniques, and applications across domains like healthcare, retail, and manufacturing.

We’ll also walk through examples in R, using real-world data from a water treatment plant to apply our knowledge practically.

Table of Contents

1. Types of Clustering Techniques
2. Distance Calculation for Clustering
3. K-Means Clustering
4. Choosing the Best K in K-Means
5. Hierarchical Clustering
6. Evaluation Methods in Cluster Analysis
7. Clustering in R – Water Treatment Plants

Types of Clustering Techniques

Common clustering algorithms include K-Means, Fuzzy C-Means, and Hierarchical Clustering. Depending on the data type (numeric, categorical, mixed), the algorithm may vary. Clustering techniques can be classified as:

• Soft Clustering – Observations are assigned to clusters with probabilities.
• Hard Clustering – Observations belong to only one cluster.

We’ll focus on K-Means and Hierarchical Clustering in this guide.

Distance Calculation for Clustering

Distance metrics are used to measure similarity between data points. Common metrics include:

• Euclidean Distance – Suitable for numeric variables.
• Manhattan Distance – Measures horizontal and vertical distances.
• Hamming Distance – Used for categorical variables.
• Gower Distance – Handles mixed variable types.
• Cosine Similarity – Common in text analysis.

K-Means Clustering

K-Means partitions data into k non-overlapping clusters. The process includes:

1. Randomly assign k centroids.
2. Assign observations to the nearest centroid.
3. Recalculate centroids.
4. Repeat until convergence.

Clustering minimizes within-cluster variation using squared Euclidean distance.

Choosing the Best K in K-Means

Methods for selecting k include:

• Cross Validation
• Elbow Method
• Silhouette Method
• X-Means Clustering

Hierarchical Clustering

This method creates a nested sequence of clusters using two approaches:

• Agglomerative – Bottom-up merging.
• Divisive – Top-down splitting.

Dendrograms visualize the clustering hierarchy. A horizontal cut across the dendrogram reveals the number of clusters.

Evaluation Methods

Clustering evaluation is divided into:

• Internal Measures – Based on compactness and separation (e.g., SSE, Scatter Criteria).
• External Measures – Based on known labels (e.g., Rand Index, Precision-Recall).

Clustering in R – Water Treatment Plants

The water treatment dataset from the UCI repository is used to demonstrate hierarchical and k-means clustering.

# Load and preprocess data
library(data.table)
library(ggplot2)
library(fpc)

water_data <- read.table("water-treatment.data.txt", sep = ",", header = F, na.strings = "?")
setDT(water_data)

# Impute missing values
for(i in colnames(water_data)[-1]) {
  set(water_data, which(is.na(water_data[[i]])), i, median(water_data[[i]], na.rm = TRUE))
}

# Scale numeric features
scaled_wd <- scale(water_data[,-1, with = FALSE])

Next, hierarchical clustering is performed using Euclidean distance and Ward's method. A dendrogram is plotted, and clusters are determined via horizontal cuts. PCA is used to visualize clusters.

# Hierarchical clustering
d <- dist(scaled_wd, method = "euclidean")
h_clust <- hclust(d, method = "ward.D2")
plot(h_clust, labels = water_data$V1)

# Cut dendrogram
rect.hclust(h_clust, k = 4)
groups <- cutree(h_clust, k = 4)

Principal components are used for cluster visualization:

# PCA for visualization
pcmp <- princomp(scaled_wd)
pred_pc <- predict(pcmp)[,1:2]
comp_dt <- cbind(as.data.table(pred_pc), cluster = as.factor(groups), Labels = water_data$V1)

ggplot(comp_dt, aes(Comp.1, Comp.2)) +
  geom_point(aes(color = cluster), size = 3)

Then, k-means clustering is applied and similarly visualized using PCA components. The clustering consistency is confirmed visually.

# K-means clustering
kclust <- kmeans(scaled_wd, centers = 4, iter.max = 100)

ggplot(comp_dt, aes(Comp.1, Comp.2)) +
  geom_point(aes(color = as.factor(kclust$cluster)), size = 3)

Introduction

When the world’s smartest companies such as Microsoft, Google, Alphabet Inc., and Baidu are investing heavily in Artificial Intelligence (AI), the world is going to sit up and take notice. Chinese Internet giant Baidu spent USD1.5 billion on research and development.

And as proof of China’s strong focus on AI and Machine Learning, Sinovation Ventures, a venture capital firm, invested USD0.1 billion in “25 AI-related startups” in the last three years in China and the U.S.

Research shows that although genuine intelligence may still be a bit far off, AI and Machine Learning technologies are still expected to reign in 2017. Try reading up on Microsoft Project Oxford, IBM Watson, Google Deep Mind, and Baidu Minwa, and you’ll understand what I am trying to get at.

In 2015, Gartner’s Hype Cycle for Emerging Technologies introduced Machine Learning (ML), and the graph showed (Figure 1) that it would reach a plateau in 2 to 5 years. Big players such as Facebook and Amazon are increasingly exploiting the advantages of this concept, which is derived from artificial intelligence and statistics, to extract meaning from huge amounts of (big) data.

Research predicts that the AI market will grow to about USD37 billion by 2025; in 2015 it was about USD645 million!

Source: Gartner

Difference between Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) and ML are not interchangeable terms. ML is sort of a subset of AI, which is a part of computer science trying to develop “machines capable of intelligent behavior.” Then, what is Machine Learning (ML)? “The science of getting computers to act without being explicitly programmed,” says Stanford. So you get that difference? You need both AI and ML experts to make smart machines that are truly intelligent.

Why are Machine Learning and Artificial Intelligence “Hot”?

"Machine learning is a core, transformative way by which we’re rethinking everything we’re doing” — Sundar Pichai, Google CEO

The pervasive commercial success of machine learning/artificial intelligence is visible everywhere—from Amazon recommending what movies you might like to see to self-driving Google cars that can tell a tree from a pedestrian.

AI/ML has changed how data-driven business leaders make decisions, gage their businesses, study human behavior, and view predictive analytics. If your organization needs to unleash the benefits of this extraordinary field, you need the right minds—quants and translators.

With breakthroughs such as parallel computation that’s cheap, Big Data, and improved algorithms, utilitarian AI is what the world is moving toward. The increased need to handle huge amounts of data and the number of IoT connected devices that define the world today reinforce the importance of machine learning.

AI/ML, with tons of potential, is a great career choice for engineers or data mining/ pattern recognition enthusiasts out there. Also, Machine Learning is integral to data science, which is touted as the sexiest job of the 21st century by the Harvard Business Review.

An Evans Data Corp. study found that 36% of the 500 developers surveyed use elements of ML in their Big Data or other analytical projects. CEO Janel Garvin said, “Machine learning includes many techniques that are rapidly being adopted at this time and the developers who already work with Big Data and advanced analytics are in an excellent position to lead the way.”

She added: “We are seeing more and more interest from developers in all forms of cognitive computing, including pattern recognition, natural language recognition, and neural networks and we fully expect that the programs of tomorrow are going to be based on these nascent technologies of today.”

So, for people who have a degree in Computer Science, Machine Learning, Operational Research, or Statistics, the world could well be their oyster for some time to come, right?

List of Courses

I’ve put together (and agonized a bit over what to add and what not to) a few free top ML and AI courses that will help you become the next ML expert Google or Apple hires. Of course, it is hard work, but if you are willing to pursue something, you’ll discover ways like these to succeed.

Machine Learning Courses

1. Machine Learning by Andrew Ng

Co-founder of Coursera, Andrew Ng, takes this 11-week course. He is an Associate Professor at Stanford University and the Chief Scientist at Baidu. As an applied machine learning class, it talks about the best machine learning techniques and statistical pattern recognition, and teaches you how to implement learning algorithms.

Broadly, it covers supervised and unsupervised learning, linear and logistic regression, regularization, and Naïve Bayes. He uses Octave and MatLab. The course is rich in case studies and recent practical applications. Students are expected to know the basics of probability, linear algebra, and computer science. The course has rave reviews from the users.

Go to Course: Start learning

2. Udacity’s Intro to Machine Learning

A part of Udacity’s Data Analyst Nanodegree, this approximately 10-week course teaches all you need to know to handle data sets using machine learning techniques to extract useful insights. Instructors Sebastian Thrun and Katie Malone will expect the beginners to know basic statistical concepts and Python.

This course teaches you everything from clustering to decision trees, from ML algorithms such as Adaboost to SVMs. People also recommend you take the foundational Intro to Data Science course which deals with Data Manipulation, Data Analysis, Data Communication with Information Visualization, and Data at Scale.

Go to Course: Start learning

3. EdX’s Learning from Data (Introductory Machine Learning)

Yaser S. Abu-Mostafa, Professor of Electrical Engineering and Computer Science at the California Institute of Technology, will teach you the basic theoretical principles, algorithms, and applications of Machine Learning.

The course requires an effort of 10 to 20 hours per week and lasts 10 weeks. They have another 5-week-course, Machine Learning for Data Science and Analytics, where newbies can learn more about algorithms.

Go to Course: Start learning

4. Statistical Machine Learning

Your instructor of the series of video lectures (on YouTube) in Advanced Machine Learning is Larry Wasserman, Professor in the Department of Statistics and in the Machine Learning Department at the Carnegie Mellon University.

The prerequisites for this course are his lectures on Intermediate Statistics and Machine Learning (10-715) intended for PhD students. If you can’t access these courses, you need to ensure you have the required math, computer science, and stats skills.

Go to Course: Start learning

5. Coursera’s Neural Networks for Machine Learning

Emeritus Distinguished Professor Gregory Hinton, who also works at Google’s Mountain View facility, from the University of Toronto teaches this 16-week advanced course offered by Coursera.

A pioneer in the field of deep learning, Hinton’s lecture videos on YouTube talk about the application of neural networks in image segmentation, human motion, modeling language, speech and object recognition, and so on. Students are expected to be comfortable with calculus and have requisite experience in Python programming.

Go to Course: Start learning

6. Google’s Deep Learning

Udacity offers this amazing free course which “takes machine learning to the next level.” Google’s 3-month course is not for beginners. It talks about the motivation for deep learning, deep neural networks, convolutional networks, and deep models for text and sequences.

Course leads Vincent Vanhoucke and Arpan Chakraborty expect the learners to have programming experience in Python and some GitHub experience and to know the basic concepts of ML and statistics, linear algebra, and calculus. The TensorFlow (Google’s own deep learning library) course has an added advantage of being self-paced.

Go to Course: Start learning

7. Kaggle R Tutorial on Machine Learning

DataCamp offers this interactive learning experience that’ll help you ace competitions. They also have an Introduction to R course for free.

Go to Course: Start learning

8. EdX’s Principles of Machine Learning

A part of the Microsoft Professional Program Certificate in Data Science, this 6-week course is an intermediate level one. It teaches you how to build and work with machine learning models using Python, R, and Azure Machine Learning.

Instructors, Dr. Steve Elston and Cynthia Rudin talk about classification, regression in machine learning, supervised models, non-linear modeling, clustering, and recommender systems. To add a verified certificate, you’ll need to pay.

9. Coursera’s Machine Learning Specialization

The University of Washington has created five courses, with practical case studies, to teach you the basics of Machine Learning. This 6-week course which requires between 5 and 8 hours of study a week, will cover ML foundations, classification, clustering, regression, recommender systems and dimensionality reduction, and project using deep learning.

Amazon’s Emily Fox and Carlos Guestrin are the instructors, and they expect the learners to have basic math and programming skills along with a working knowledge of Python. Course access is free though getting a valid certificate is not.

Go to Course: Start learning

Artificial Intelligence Courses

1. EdX's Artificial Intelligence

This exciting course from EdX talks about AI applications such as Robotics and NLP, machine learning (branch of AI) algorithms, data structures, games, and constraint satisfaction problems. It lasts 12 weeks and is an advanced-level tutorial from Columbia University.

Go to Course: Start learning

2. Udacity’s Intro to Artificial Intelligence

The course is expected to teach you AI’s “representative applications.” It is a part of its Machine Learning Engineer Nanodegree Program. Instructors Sebastian Thrun and Peter Norvig will take you through the fundamentals of AI, which include Bayes networks, statistics, and machine learning, and AI applications such as NLP, robotics, and image processing. Students are expected to know linear algebra and probability theory.

Go to Course: Start learning

3. Artificial Intelligence: Principles and Techniques

This Stanford course talks about how AI uses math tools to deal with complex problems such as machine translation, speech and face recognition, and autonomous driving. You can access the comprehensive lecture outline—machine learning concepts; tree search, dynamic programming, heuristics; game playing; Markov decision processes; constraint satisfaction problems; Bayesian networks; and logic— and assignments.

Go to Course: Start learning

4. Udacity's Artificial Intelligence for Robotics by Georgia Tech

Offered by Udacity, this course talks about programming a robotic car the way Stanford and Google do it. It is a part of the Deep Learning Nanodegree Foundation course. Sebastian Thrun will talk about localization, Kalman and Particle filters, PID control, and SLAM. Strong grasp of math concepts such as linear algebra and probability, knowledge of Python, and programming experience are good-to-have skills.

Go to Course: Start learning

Summary

In this post, a few of the listed courses are meant to help you get started in the exciting and fast-growing field of Machine Learning and Artificial Intelligence. Others take you through slightly more advanced aspects. The courses listed are free and the only thing stopping you from getting the most out of them will be a lack of commitment.

These world-class courses, which focus on a specific area of learning, are great stepping stones to lucrative and amazing careers in machine learning, data science, and so much more. If you don’t want the Baxters of the world to make you obsolete, you best teach them just who the master is.

So once you identify your learning goals, and assuming you have reliable access to technological requirements, be self-disciplined, build a study plan, set time limits, stay on schedule, work effectively with others, and, most of all, find ways to stay motivated.

Last week when I sharedThe top programming languages that will be mostpopular in 2017, the frequent comment was, what does it take to be a better developer?

I’ve met some amazing developers in real life and through React Native Community, and I decided to ask them, “How do I become a better developer?” Thank you to everyone who took the time to answer these questions with passion!

This is a compilation of answers I received from them. Some of these quotes are not limited to answers from that specific question.

Interviewees / Current Position

• Aravind Kumaraguru (Engineering Director @Pioneers in Engineering)
• Brent Vatne (Front-end Developer @Exponent)
• Charlie Cheever (Co-founder @Exponent)
• Christopher Chedeau (Front-end Engineer @Facebook)
• Dan Horrigan (Senior Back-end Developer @Futuri Media)
• Frank W. Zammetti (Lead Architect @BNY Mellon)
• Janic Duplessis (Co-founder @App & Flow)
• Jake Murzy (Co-founder @commitocracy)
• Jun Ho Hwang (Software Engineer @Coupang)
• Keon Kim (Machine Learning Maniac @NYU)
• Munseok Oh (Co-founder and CTO @Sketchware)
• Satyajit Sahoo (UX Lead @ Glucosio & Front-end Engineer @Callstack.io)
• Sonny Lazuardi Hermawan (Engineer @Sale Stock)
• Sunggu Hwang (CTO @ScatterLab)
• Timothy Ko (Software Engineer @Snapchat)

Q&A

Aravind Kumaraguru

Aravind is an undergrad at UC Berkeley pursuing a degree in Electrical Engineering and Computer Science and is Engineering Director for the nonprofit organization Pioneers in Engineering.

Q: How do you think I can become a better developer?

A: Obviously, never stay complacent with what you know – this field changes ridiculously fast, and you need to keep up with it. Follow along with the news in the tech industry, perhaps read up on some source code for a Python module that you recently used.

A friend of mine had some free time over winter break, so he decided to teach himself Django and build a webapp that he could interact with over SMS. It’s sort of a toy project, but he really enjoyed learning the different development paradigms. For context, he specializes in embedded systems and robotics, so this is nowhere near his comfort zone.

But pushing yourself to try different things will make you much stronger as an engineer. I personally wish I had done more web stuff before this year – in my organization (PiE), we’re developing a new iteration of a robotics kit to be used by high school students. While I have a good grasp of the low-level and systems stuff, I’m at a loss when it comes managing our UI design. Never had an interest in doing that type of stuff full-time, but having even a surface-level knowledge can be immensely helpful

Q: Do you have any projects you did to push yourself out of your comfort zone?

A: I built an automated door opener last summer, which operated a mechanical lever to open a door when an RFID card was scanned. The project used a really powerful motor and a mess of sensors to track the state of the arm, which proved to be quite difficult to coordinate. I learned real quick that I would need to do a bunch of offline testing before running my code on the device, which was very different from what I was used to up till then.

In terms of academics, I just finished CS 189, which was a massive crash course in data science, optimization, and probability theory. The programming I did in that class was also very different from what I’m used to, even though it was all in Python.

Brent Vatne

Brent is Front-end web/mobile developer working on Exponent and React Native. He contributes to tons of open-source projects.

Q: I really want to become a better developer; what would you say the first step is?

A: Do stuff you’re excited about and contribute to open source projects:-D

Q: How old are you and how much experience do you have as a programmer?

A: I am 30 years old, and very much 😮

Q: How did you join Exponent? What was the cause?

A: James (ide) and I were the most active contributors to a react-native outside of facebook and so we spoke a lot. He created exponent with Charlie. I ended up doing some consulting work with them and Charlie asked if I’d be interested in working with them full time and year, it was lots of fun so I joined.

Q: I should know objective C and Java thoroughly before I jump into React Native, right?

A: You can learn it as you go if you need to. there’s also tons of pure javascript stuff that need to be done. and documentation. lots of things 🙂

Charlie Cheever

Charlie Cheever is the co-founder of Quora, an online knowledge market. He was formerly an engineer and manager at Facebook, where he oversaw the creation of Facebook Connect and the Facebook Platform. Prior to Facebook, Cheever was employed by Amazon.com in Seattle. He left Facebook to start Quora in June 2009 to work on Exponent.

Q: What’s the motivation of Exponent being free and Open Source?

A: I really want to make something that like a 12-year-old version of me would use. So, someone who doesn’t know tons about programming but can learn new things and doesn’t have a credit card or lots of money, but has time and creativity and a phone and friends. I learned to program making calculator games on TI-85, it’s sad to me that kids can’t make stuff on their phones today.

Q: Why did you leave Quora?

A: I managed the mobile teams there and it was so slow to work on those apps even tho we had good people, I found it so frustrating And after I left I tried to build some mobile stuff and it was so annoying that I decided there needed to be a different way to make stuff. So James and I made something like react Native called Ion. It was strikingly similar actually. But React Native already had android support and 20 people working on it, and we had 2 people. So we decided to make everything else around it that we wanted to make!

Q: What did you do on Facebook?

A: I made the developer platform that all those games like FarmVille were on. Well, not all of it obviously but was one of two main developers. And I worked on the first version of facebook video, then did a lot of random other things. Then was a manager and did log in with Facebook on other sites, and then left to do Quora.

Christopher Chedeau

Christopher has been working at Facebook as a Front-end Engineer for about 5 years. Previously, he worked at Curse Network.

Q: What do you do on Facebook?

A: I was on the photos team when I started, then I discovered React and started adopting and promoting it both internally and externally. I was there at the beginning of reacting native and pushed it through until 3 months ago. I just recently switched to the Nuclide team. I’m still #3 contributor on React Native.😛

Q: Do you have any prior work experience?

A: I was working for Curse (doing website for blizzard games) during my college to pay for it. It was fun to see the company go from 5 people in a guild to a 100 people company.

Q: What’s your day to day like on Facebook? The current project you’re working on?

A: I’m currently working on the Nuclide team, Facebook’s IDE built on top of Atom. I would say my time is spent half coding, half cheerleading all the cool stuff people are doing inside of FB.

Q: How do you think one can become a better developer?

A: I think that there are multiple levels.

The first level is mastering all the concepts. For example yesterday I had to write a function that removes certain keys from a big nested object. Because I’ve done this task so many times in the past, I was able to implement it in one go without even thinking and it worked the first time. For this one, exercises are really good. You want to code the same kind of things many many times to train your muscle memory.

The second level is how do you build things in a way that are not going to break in the future. Ideally, once you build something, you can move to the next thing and it’ll keep working without you there. This is challenging when there’s a ton of developers touching the codebase and product directions changing often.

Finally, the third level is how do you prevent a whole class of problems from even existing in the first place. A good example is with manual dom mutations, it’s very easy to trigger some code that interacts with a dom node that has been removed from the dom. React came in and made this problem go away. You have to go out of your way to do so, and even if you want to do those things, you have the tools to make it work: lifecycle events.

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Probably the most important thing is: tradeoffs, tradeoffs, tradeoffs. They are everywhere.

If you are working on some random throwaway feature that no one is going to use, who cares if the code is maintainable, you need it to work and now one mistake I see a lot is that people over-engineer the easy things but are not willing to make their architecture less clean from a CS perspective even though it actually provides the user experience you need.

At the end of the day, we write all this code for the users, we should first understand what the user experience should be and then do whatever it takes to get it. If the user just needs to display some content and needs to be able to edit it easily, just install WordPress, pick a good looking theme and call it a day

– Btw, pro-tip, if you want to be successful, always think about the value you are providing. If you are earning $100k a year, this means that the company should be making $200k because you’re here

Dan Horrigan

Dan is a Senior Back-end developer @Futuri Media. He has 20 years of programming experience in many different languages. He’s been contributing to React Native early/mid-2015.

Q: What’s your background as a programmer?

A: I started learning to program (with QBasic) when I was 11 and was hooked. I learned everything I could, as fast as I could. I learned a few languages like Visual Basic and started to dabble with C and C++. Then I found web development and dove in head first. First, learning HTML and CSS, then adding simple CGI scripts written in Perl, and eventually Classic ASP.

My first paying project was when I was 14: A website for the company my dad worked for, with a customer portal to let them see their job progress. This was all in ASP. After that, I started learning PHP, and have been using that as my language of choice ever since. However, I picked up a lot of experience with other languages along the way: JS, Python, Ruby (on Rails), Java, C#, Go, Objective-C.

Q: What are some projects you’re currently working on?

A: I work for Future Media (http://futurimedia.com). We provide SaaS solutions for Broadcast Radio and TV companies. We provide white label mobile applications, social engagement and discovery, audio streaming and podcast solutions, etc. I haven’t had much free time lately to contribute to many OSS projects, but hope to change that soon!

Currently, I am a Senior Back-End Web Developer, but I am transitioning into being the Director of Technical Operations.

Q: Is there something you wish you’d learned or knew earlier as a developer?

A: I wished I would have realized earlier in my career that it is OK to be wrong, and that failure is just a chance to learn.

Q: What’s the first step to becoming a good developer?

A: Come up with a small-ish project that you think would be cool, or would make your life easier, and just jump right in. Too many people try to learn without a goal other than “I want to learn to code.” Without a goal, you are just reading docs or copy/pasting from tutorials…you can’t learn that way.

To become a better developer, you need to do one simple thing: Never. Stop. Learning. Read other people’s code, figure out how that one app does that really cool thing you saw, read blogs, etc. No matter how good you are, or think you are, there is always someone better, and always more to learn.

Q: Is there a certain project you’re currently interested in? Next on your learning list?

A: I have been using, and occasionally contributing to, React Native since early/mid-2015, and continue to be interested in it.

Next, on my learning list is learning Erlang/Elixir. We build heavily distributed systems where I work and think we would really benefit from a language like that.

Frank W. Zammetti

Frank is a lead architect for BNY Mellon by day and the author of eight books on various programming topics for Apress by night

Q: How do I become a better developer?

A: I get asked this question quite a bit both at work from junior developers and from readers of my books. I always give the same answer: make games!

It sounds like a joke answer, but it most definitely is not! Games have a unique ability to touch on so many software engineering topics that you can’t help but learn things from the experience. Whether it’s choosing proper data structures and algorithms, or writing optimized code (without getting lost in micro-optimizations – at least too soon), or various forms of AI, it’s all stuff that is more broadly applicable outside of games. You frequently deal with network coding, obviously audio and visual coding (which tends to open your mind to mathematical concepts you otherwise might not be), efficient I/O and of course overall architecture, which has to be clean and efficient in games (and for many games, extensible). All those topics and more are things that come into play (hehe) when making games.

It also teaches you debugging and defensive programming techniques extremely well because one thing people don’t accept in games is errors. It’s kind of ironic actually: people will deal with some degree of imperfection in their banking website but show a single glitch in a game and they hate it! You have no choice but to write solid code in a game and you figure out what works and what doesn’t, how to recover from unexpected conditions, how to spot edge cases, all of that. It all comes into play and those are skills that developers need generally and which I find are most frequently lacking in many developers.

It doesn’t matter one bit if the game you produce is any good, or whether anyone else ever even plays it. It doesn’t matter if it’s web-based (even if your day job is), or mobile, doesn’t matter what technologies you use. The type of insight and problem-solving skills you build and tune when creating games will serve you well no matter what your day job is, even in ways that are far from obvious.

I’ve been programming games for the better part of 35 years now. No, none of them have been best-sellers or won awards or anything like that. In fact, it’s a safe bet that most people wouldn’t have even heard of my games, even the one’s still available today. None of that matters because the experience of building them is far and away the most rewarding part of it. Perhaps the best thing about programming games is that they are, by their nature, fun! You’re creating something that’s intended to be enjoyable so the process of creating it should absolutely be just as enjoyable. How many things can you do that are really fun while still being challenging and simultaneously help build the skills needed for a long career?

So yeah, make games, that’s my simple two-word answer!

Q: Is there something you wish you’d known or learned earlier as a programmer?

A: Hmm, tough question actually. I guess if there was one thing (and I’ll cheat and combine two things here because they’re related) I would say that early on I didn’t understand two very important phrases: “As simple as possible, but no simpler” and “Don’t let the perfect be the enemy of the good”.

I have a natural perfectionist mentality, so I spend a lot of time pondering architecture, API design, etc. I once spent 33 hours straight working on a Commodore 64 demo because ONE lousy pixel was out of place and my perfectionist brain just couldn’t live with it! Sometimes, I have to force myself to say “okay, it’s good enough, you’ve planned enough, now get to work and actually BUILD stuff and refactor it later if needed”, or I have to force myself to say “okay, it basically does what it’s supposed to, it doesn’t need to be absolutely flawless because nobody but me is even going to notice”. Especially when you’ve got deadlines and people relying on you, you have to make sure you’re working towards concrete goals and not constantly getting stuck trying to achieve perfection because you rarely are going to, at least initially anyway, no matter how hard you plan or try – and the dirty little secret in IT is that perfection rarely matters anyway! Good enough is frequently, err, good enough 🙂

And, your design/development approach should always strive to be as absolutely simple as possible. Of course, what constitutes “simple” is debatable and doesn’t necessarily even always have the same meaning from project to project, but for me some key metrics are how many dependencies I have (web development today is a NIGHTMARE in this regard – less is GENERALLY better) and how many layers of abstraction there are. Developers, especially in the Java world, like to abstract everything and they do so under the assumption that it’s more flexible. But if there’s one thing I’ve learned over the years it’s that the way to write flexible code is to write simple code. It’s better than abstractions and extension points and that sort of stuff because it’s just far easier to understand the consequences of your changes.

As a corollary, a terse code is NOT simpler code! Simple code is code that anyone can quickly understand, even less capable developers, and even yourself years after. Terse and “clever” code tends to be the exact opposite. Often times, the more verbose code is actually simpler because there are fewer assumptions and often less knowledge needed to understand it, less “code hoping” you have to do to follow things. Related to this is that writing less code isn’t AUTOMATICALLY better. No, you shouldn’t re-invent the wheel, but you also shouldn’t be AFRAID to invent a marginally better the wheel when it makes sense. Knowing the difference is hard of course and comes from experience, but if you think it’s ALWAYS better to write less code then you’re going to make your life harder in the long run.

Of course, don’t over-simplify code either. Too simple and suddenly extending it almost MUST mean a refactor. You never want to completely refactor because you HAVE to in order to build an app over time. There’s a balance that’s difficult to strike but it should always be the goal.

Oh yeah, and I wish I knew how to express myself in fewer words… but actually, I’m still obviously working on that one 🙂

Janic Duplessis

Janic is the co-founder of App & Flow, a react-native contributor, and open-source contributor.

Q: Any tips to becoming a better developer?

A: Don’t think there’s anything in particular, you just have keep learning and getting out of your comfort zone. Like trying a new language or framework from time to time. At least that’s what I do but I’m pretty sure there are some other good ways haha 🙂

Q: How can I start contributing to React Native?

A: The best is to start with something small like a bug fix or adding a small feature like an extra prop on a component. Most contributors know either iOS or Android and a bit of JS. There are also some JS devs that work on things like the package and clip. We keep some issues with a Good First Task label that should be a good place to start

Jake Murzy

Jake is an Open-source Archaeologist. He writes buzzword compliant code. Co-founder at @commitocracy.

Q: Hey Jake, any tips to becoming a better programmer? 🙂

A: Number one thing you should do is to learn your tools before you learn the language you work in because it will lead to faster feedback loops and you will get to experience more in less time. So install a linter and it will catch most of your errors as you type. It statically analyzes your code and recommends best practices to follow. You should always follow best practices until you gain enough experience to start questioning them.

Jun Ho Hwang

Jun is a software engineer at Coupang, which is the $5 Billion Startup Filling Amazon’s Void In South Korea. He is a very friendly developer who loves to connect.

Q: How do you become a better developer?

A: The word ‘better’ can be described in various ways–especially in the field of programming. A good developer could be someone who is exceptionally talented in development, someone who is amazing at communicating, or someone who understands Business very well. I personally think a “good” developer is someone who is in the middle–a person who can solve his or her business problem with their development skills, and communicate with others about the issue. Ultimately, to achieve this, it requires a lot of practice, and I recommend you to create your own service. Looking and thinking from the perspective of the user and improving the service to fulfill their needs really helps you grow as a better developer.

Q: Is there something you wish you’d known or learned earlier as a developer?

A: I really wish I started my own service earlier on. The hardest thing to grasp before developing is realizing how you can apply what you learned. Many developers are afraid to start a “service” because it sounds difficult; however, pondering about what to make and where to start, and then connecting those points of thought help you grow as a better developer.

Q: What do you do at Coupang? What are you currently working on?

A: Coupling provides a rocket-delivery-service, and I am working on developing a system called “Coupling Car,” which is related to insurance and monetary management. Furthermore, I’m thinking about adding transportation control system and the ability to analyze data from the log.

Keon Kim

Keon is a student at NYU who is really passionate about Machine Learning. He is a very active GitHub member who tries to contribute to open source projects related to machine learning.

Q: What are your interests? What kind of projects have you worked on?

A: I’ve been working on machine learning projects these days. I am one of the project members of DeepCoding Project, a project with a goal of translating written English to the source code. I’ve been contributing to a C++ machine learning framework called my pack(https://github.com/mlpack/mlpack), which is equivalent to skit-learn in Python.

I’ve also done some fun side projects: DeepStock (https://github.com/keonkim/deepstock) project is an attempt to predict the stock market trends by analyzing daily news headlines. CodeGAN (https://github.com/keonkim/CodeGAN) is a source code generator that uses one of the new deep learning methods called SeqGAN.

Q: How do you become a better developer?

A: I think it is really important to understand the basics. By basics, I mean math, data structures, and algorithms. Deep learning is really hot right now, and I see people jumping into learning it without basic knowledge in computer science and mathematics. And of course, most of them give up as soon as mathematical notations appear in the tutorial. I know this because I was one of them and it took me really long time to understand some concepts that students with a strong fundamentals could understand in a fraction of the time I spent. New languages, libraries, and frameworks are introduced literally every day these days, and you need the fundamentals in order to keep up with them.

Munseok Oh

Munseok is a Full-stack developer and CTO at Sketchware. He previously worked at System Integration for ~7 years.

Q: How do I become a better developer?

A: When I was very young and cocky, I evaluated other developers based on their coding style. There were certain criteria they had to pass in order for me to judge them as a good developer. But now, I really don’t think that way. Now, I believe that every developer is progressive, which means he or she is becoming a better developer every day. It doesn’t really matter if the style is bad or code is good–as long as the program runs, I think it’s great! Whether the program has room for growth or has bugs, I think the motivation to develop is what really matters. Developers usually are never satisfied with their skills. They are always eager to become better–probably why you’re doing this. It’s really hard to justify “good developer”. People like you will become better than me in no time. I still don’t think I am a good developer.

Q: What was the most difficult thing when you were developing Sketchware?

A: Developing Sketchware wasn’t too difficult because we had a good blueprint for the item. The direction was very clear for us to follow, so developing it was a breeze. However, there was a line we had to maintain for Sketchware–this line had two conditions:

1. Sketchware must be an easy tool for anyone to create applications.
2. Whatever the user takes away from Sketchware can be applied in their future career

Since we wanted Sketchware to be an efficient tool that can help users learn programming concepts, I am very considerate and think a lot when it comes to adding new features in the application.

Q: As a developer, is there something you wish you knew or fixed earlier?

A: I really wish I jumped into the Start-up world earlier. When it comes to developing, you need to be passionate and really enjoy what you do. Even if you pull 3 all-nighters, ponder all day long about a new algorithm, or stress about a new bug, everything will be okay if you’re enjoying it. It really goes back to the question #1–I get my energy from the joy I have when I develop, and that joy eventually makes you a better developer. When life hits you, most developers lose the passion for developing if you think of it as work. I used to be like that. But now, I’m really not worried–since developing brings joy to me now. Even if we run out of funds or our company burns down, it’s really okay since I am making the most out of what I am doing.

Satyajit Sahoo

Satyajit is the UX Lead at Glucosio, and Front-end Engineer at Callstack.io. He is an amazing open-source contributor; he is one of the top 5 contributors in React Native

Q: What is your background as a programmer?

A: I don’t really come from a programming background. I did my graduation in Forestry. I left post-graduation after getting a job offer and never looked back.

Q: What’s your day like on day to day basis?

A: It’s pretty boring. I wake up, order some breakfast online or go out, then start office work. In evening I go out to a bar or take a long walk if there’s enough time left. At night I mostly watch TV series or hack on side-projects.

Q: Motivation behind contributing to open source projects?

A: I’ve been involved in Open Source for a long time. When I was doing my graduation I got into Linux and got introduced to the world of Open Source. I loved it how we could learn so much from other projects. It fascinated me that developers were selfless to let us see and use the there code for free (mostly). I did a lot of Open Source projects in form of themes and apps during my college days, and it always made me happy when people forked them and changed to meet their needs, and send pull requests to fix things.

As a developer, I contribute to Open Source projects most of the time because I need a feature, or it improves something on a project I love. I think it’s better if we work together to fix stuff that is important to us rather than just filing issues.

Q: How do I become a better developer?

A: I think it’s important that we are open to new things. There’s a lot to learn, and we cannot learn if we stay in our bubble. Try new things, even if you think you can’t do it, even it looks complex on the surface. I have failed to do things so many times, but eventually succeed. In the process, I understand the problem and the solution, and then it becomes really simple.

Sonny Lazuardi Hermawan

Sonny is a JavaScript Full Stack Engineer, a React & React Native player, and an Open source enthusiast. He currently works as an Engineer at Sale Stock.

Q: How do you become a better developer?

A: I think always eager to learn is the key. Try everything, make mistakes, and learn from that mistakes. I agree that code review from partners and senior engineers will make our code better. Try publishing your own open source projects, meet other great developers and learn from them.

Q: What’s your motivation behind creating open source projects?

A: I just want the people to know about our idea, and try implementing it so that others can use our project. I’m really inspired by people that work on open source projects that used by many devs such as Dan Abramov that created redux.

Sunggu Hwang

Sunggu worked at Daum Communications for 4 years. Then, he left Daum to work at Scatter Lab as the CTO. This is his 5th year at Scatter Lab.

Q: How do you become a better developer?

A: Hmm… Becoming a good developer… Every developer has his or her own personality when it comes to programming. As an analogy, think about blacksmiths! Not all blacksmiths are alike–some enjoy crafting the best sword, while some might enjoy testing out the sword more than crafting it. I am a thinker–who plans and organizes thoughts before I carry out an action. I think a good developer knows how to write concise and clean code; you should practice this habit. Even though the trend for programming is always changing, and many people use different languages, write a piece of code that anyone can understand without comments.

Q: What do you think is the next BIG thing?

A: I’ve observed the evolution of programming languages, and I think it’s becoming more abstract every generation–procedural programming, imperative programming, functional programming… I think in the future, maybe in about 20 to 30 years, we will live in the time where the computer writes the code for us, and we just put them together like legos.

Q: What should I focus on studying?

A: I think deep learning is a must. Try different tutorials and learn it with passion. Math, algorithms–anything will help you in the long run.

Timothy Ko

Timothy is a software engineer at Snapchat. He previously worked at many places such as Riot Games, Square, etc.

Q: What do you do at Snapchat?

A: I’m a software engineer on the monetization team, so I work on anything related to making money. Some example projects are Snapchat Discover, a news platform within the iOS and Android apps; Ad Manager, a control panel used by sales and ad operations to flight ads; Ads API, which allows third-party partners to integrate their own ad platforms into Snapchat. Also, I was a past intern at Snapchat so I occasionally give talks and Q&As to upcoming interns. I’m also heavily invested in hiring and conduct a lot of interviews there.

Q: What do you do on a day-to-day basis?

A: What I’ve mentioned previously. Also, even after I pass on the work to other people, sometimes I have to go back and help support it or be part of the technical discussions on future changes. When new people join the team, usually I’m the one to ramp people up on how the code base looks like the kinds of frameworks we use, how a typical engineer workflow looks like, etc.

Q: What languages/framework do you guys mostly use?

A: For server code, it’s usually Java and for UI we use React Redux. Most teams work in google app engine, which is why we use Java, but some teams switch it up a little bit due to some app engine limitations. And of course, the product teams work in objective C for iOS and Java for Android.

Q: How do you think I can become a better developer?

A: I think the best thing to do is to do as many things as possible. I did seven internships while in school so I already had two years of work experience before I graduated. Work experience is super important because coding in a hackathon, doing personal projects, and doing school assignments are totally different than working with enterprise software and apps with real users. But you have to start somewhere, so that’s where going to school, doing personal projects, and competing in hackathons comes in. And while at work, I think the best way to succeed is to ask lots of questions and learn by doing. You can read and study all you want, but you might not understand what’s going on until you actually do it. Another thing is code reviews — you can do so much knowledge transfer by having a more senior engineer tear your code apart and tell you how to make it better. Also, if you ever come up with a proposal on how to solve a problem, getting a tech lead to bombard you with hard questions forces you to make sure you have every little detail covered.

*The article was originally posted by Sung Park on Github*