Journey into the Galaxy of Machine Learning ...

With COVID-19 restricting a lot of our activities, it allowed me to take the next topic off my TO-DO list - Data Science/Machine Learning ... For the past few months have been diligently chipping away and building my brain muscles around Data Science/Machine Learning, with the curiosity to dig deeper and understand what all the buzz around this is about.

Some of my well-wishers have been asking me to pull together all the links from my personal blog into one article so it is easy for them to navigate ... at this point think have enough material to even publish a book .... a task for another day !!!

There is a general misconception around the subject of Machine Learning - it is like MAGIC and will solve the World hunger problems. Let me pop that bubble first - it WILL NOT. Machine Learning is an INGENIOUS application of Math on CLEANSED DATA to extract patterns and insights.

Notice the use of capital letters - CLEANSED DATA. The majority of one's time (about 80%) in any Data Science/Machine Learning effort is spent on preparing (cleaning, transforming, etc) the data acquired from the real world.

With that said, for all those aspiring to venture into the wonderful Galaxy of Data Science/Machine Learning, here are my thoughts on your journey:

1. Programming Language

Learn and get proficient with a programming language (and its related tools and frameworks) suitable for Data Science/Machine Learning - Python, R, Julia, etc ... Pick your favorite !!!

In my case the choice was simple ... Python ... as had professionally programmed in the language for few years.

Here are some links related to Python frameworks from my personal blog:

- Exploring NumPy

- Exploring Pandas Series

- Exploring Pandas DataFrame

- More Pandas DataFrame

- Introduction to Matplotlib - Part 1

- Introduction to Matplotlib - Part 2

- Introduction to Matplotlib - Part 3

- Useful Matplotlib Code Snippets

2. Database and SQL

Learn and get comfortable with a relational database and the query language SQL.

Here are some links related to database (via Python) from my personal blog:

- Introduction to SQLAlchemy :: Part - 1

- Introduction to SQLAlchemy :: Part - 2

- Introduction to SQLAlchemy :: Part - 3

3. Mathematics

Take the time wrap your hands around some of the Math topics and understand them well - Algebra, Geometry, Probability, Statistics, Linear Algebra, and Calculus.

This is CRITICAL. It may feel like a lot and unnecessary. Trust me - your brain will thank you for it. Know that the subject of Math may seem huge like an Elephant. The only way to deal with it ... one spoon at a time. For those of us who are no longer in school, there is no pressure to get good grades. If we don't understand a concept, its okay - we have the time on our hands to Google/Youtube till we grasp the topic and develop an intuition/understanding of it. In the end, it will be a worthwhile effort !!!

Here are some links related to the topics in Math from my personal blog:

- Introduction to Permutations & Combinations

- Introduction to Probability

- Introduction to Bayes Theorem

- Introduction to Statistics - Part 1

- Introduction to Statistics - Part 2

- Introduction to Statistics - Part 3

- Introduction to Statistics - Part 4

- Introduction to Statistics - Part 5

- Introduction to Statistics - Part 6

- Introduction to Statistics - Part 7

- Introduction to Linear Algebra - Part 1

- Introduction to Linear Algebra - Part 2

- Introduction to Linear Algebra - Part 3

- Introduction to Linear Algebra - Part 4

- Introduction to Linear Algebra - Part 5

- Introduction to Calculus - Part 1

- Introduction to Calculus - Part 2

- Introduction to Calculus - Part 3

- Introduction to Calculus - Part 4

- Introduction to Calculus - Part 5

4. Data Engineering

Learn about the Data and the art of Data Engineering. This is the realm of Exploratory Data Analysis (EDA) and Feature Engineering.

Here are some links related to data wrangling from my personal blog:

- Data Preparation - Part 1

- Data Preparation - Part 2

- Data Preparation - Part 3

5. Machine Learning Algorithms

Learn the various Machine Learning methods - Regression, Classification, Ensemble Methods, etc. Do realize that these techniques involve the application of Math in interesting ways. Try to understand and get an intuition on how they work.

Here is a comprehensive growing list of links related to machine learning from my personal blog:

???- Linear Regression - Part 1

???- Linear Regression using Scikit-Learn - Part 2

???- Polynomial Regression using Scikit-Learn - Part 3

???- Understanding Bias and Variance - Part 4

???- Understanding Regularization - Part 5

???- Regularization using Scikit-Learn - Part 6

???- Understanding Cross Validation

???- Logistic Regression - Part 1

???- Logistic Regression using Scikit-Learn - Part 2

???- Naive Bayes using Scikit-Learn

???- K Nearest Neighbors using Scikit-Learn

???- Decision Trees using Scikit-Learn

???- Understanding Ensemble Learning

???- Random Forest using Scikit-Learn

???- AdaBoost using Scikit-Learn

???- Gradient Boosting Machine using Scikit-Learn

???- K-Means Clustering using Scikit-Learn

???- Principal Component Analysis using Scikit-Learn

???- Support Vector Machines using Scikit-Learn

Hope you find this curated material presented here beneficial for your learning purposes. Any feedback on the material (mistakes, misinterpretations, etc) most welcome. If the material is useful to you, LIKE it and SHARE it with others, so they benefit from it as well !!!