Overview

Machine Learning (ML) is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extracts patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention. The first part of this book introduces Python basics including: -Data Structures like Pandas -Foundational libraries like Numpy, Seaborn, and Scikit-Learn The second part of this book shows you how to build predictive machine learning models step by step using techniques such as: -Regression analysis -Decision tree analysis -Training and testing data models -Tensor Flow, Keras, and PyTorch -Additional data science concepts like Classification Analysis, Clustering, Association Learning, and Dimension Reduction The final part of the book provides a structured framework on how to solve real-world problems using data science and how to structure your data science project. After reading this book you will be able to: -Code in Python with confidence -Build new machine learning models from scratch -Know how to clean and prepare your data for analytics -Speak confidently about statistical analysis techniques

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