Manohar Swamynathan - Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

Each topic has two parts: the first part will cover the theoretical concepts and the second part will cover practical implementation with different Python packages. The traditional approach of math to ML (i.e., learning all the mathematic then understanding how to implement them to solve problems) needs a great deal of time/effort, which has proved to be inefficient for working professionals looking to switch careers. Hence, the focus in this book has been more on simplification, such that the theory/math behind algorithms have been covered only to the extent required to get you started.

I recommend that you work with the book instead of reading it. Real learning goes on only through active participation. Hence, all the code presented in the book is available in the form of Jupyter notebooks to enable you to try out these examples yourselves and extend them to your advantage or interest as required later.

Chapter , Step 1: Getting Started in Python 3 will help you to set up the environment, and introduce you to the key concepts of Python 3 programming language relevant to machine learning. If you are already well versed in Python 3 basics, I recommend you to glance through the chapter quickly and move on to the next chapter.

Chapter , Step 2: Introduction to Machine Learning : Here you will learn about the history, evolution and different frameworks in practice for building ML systems. I think this understanding is very important, as it will give you a broader perspective and set the stage for your further expedition. Youll understand the different types of ML (supervised/unsupervised/reinforcement learning). You will also learn the various concepts involved in core data analysis packages (NumPy, Pandas, Matplotlib) with example codes.

Chapter , Step 3: Fundamentals of Machine Learning : This chapter will expose you to various fundamental concepts involved in feature engineering, supervised learning (linear regression, nonlinear regression, logistic regression, time series forecasting, and classification algorithms), and unsupervised learning (clustering techniques, dimension reduction technique) with the help of Scikit-learn and statsmodel packages.

Chapter , Step 4: Model Diagnosis and Tuning : in this chapter youll learn advanced topics around different model diagnosis, which covers the common problems that arise and various tuning techniques to overcome these issues to build efficient models. The topics include choosing the correct probability cutoff, handling an imbalanced dataset, the variance, and the bias issues. Youll also learn various tuning techniques such as ensemble models, and hyperparameter tuning using grid/random search.

Chapter , Step 5: Text Mining and Recommender Systems : Statistics say 70% of the data available in the business world is in the form of text, so text mining has vast scope across various domains. You will learn the building blocks and basic concepts to advanced NLP techniques. Youll also learn the recommender systems that are most commonly used to create personalization for customers.