Introduction to machine learning

This book gives a layman explanation for machine learning using Python. We will explain a lot of basic machine learning topics using python code. There are a lot of examples that we can use to master the skill of Data science. This book will help you understand the basic algorithms that machine learning deals with. There are a lot of concepts that can be used to acquire advanced skills in data science and its subsequent subfields. In the first chapter, we will discuss very basics and introduce Python environment for the users. There are certain basic principles that can be learned using the book. We will then discuss data processing techniques which are very important for a good machine learning model. We will introduce pandas, numpy models to the reader along with their use cases. We will also try to expand our knowledge using machine learning algorithms that are described in the book. In the next sections, we will learn about machine learning models. The last two chapters will give a practical point of view to what we have discussed. Below, we explain the most important concepts we discussed in this book in no particular order.

• Introduction to machine learning and python environment
• Introduction to numpy, Pythons, and other machine learning python modules
• Introduction to data processing techniques in detail
• Introduction to data visualization in detail. We will learn about histogram and pie in detail
• We will learn about a lot of machine learning algorithms like Regression analysis, Decision trees, Support vector machine, and others in detail
• We will also discuss other algorithms in brief
• We will learn about ensemble modeling in detailed in the chapters inside
• We will give a few use cases to it
• We will also discuss hyperparameter turning in detail
• We will next learn about machine learning project structure, pipelines, and other advanced topics in the last chapter