Who This Book Is for
This book will serve as a great resource for learning machine learning concepts and implementation techniques for the following:
• Python developers or data engineers looking to expand their
knowledge or career into the machine learning area.
• A current non-Python (R, SAS, SPSS, Matlab, or any other
language) machine learning practitioners looking to expand their
implementation skills in Python.
• Novice machine learning practitioners looking to learn advanced
topics such as hyperparameter tuning, various ensemble
techniques, Natural Language Processing (NLP), deep learning,
and basics of reinforcement learning.
What You Will Learn
Chapter 1, Step 1 – Getting started in Python. This chapter will help you to set up the environment, and introduce you to the key concepts of Python programming language in relevance to machine learning. If you are already well versed with Python basics, I recommend you glance through the chapter quickly and move onto the next chapter.
Chapter 2, Step 2 – Introduction to Machine Learning. Here you will learn about the history, evolution, and different frameworks in practice for building machine learning systems. I think this understanding is very important as it will give you a broader perspective and set the stage for your further expedition.
You’ll understand the different types of machine learning (supervised / unsupervised / reinforcement learning). You will also learn the various concepts are involved in core data analysis packages (NumPy, Pandas, Matplotlib) with example codes.
Chapter 3, 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), unsupervised learning (clustering techniques, dimension reduction technique) with the help of scikit-learn and statsmodel packages.
Chapter 4, Step 4 – Model Diagnosis and Tuning. in this chapter you’ll 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. You’ll also learn various tuning techniques such as ensemble models and hyperparameter tuning using grid / random search.