Take tiny steps to enter the big world of data science through this interesting guide
Key Features:
- Learn the fundamentals of machine learning and build your own intelligent applications
- Master the art of building your own machine learning systems with this example-based practical guide
- Work with important classification and regression algorithms and other machine learning techniques
Book Description:
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.
Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
What You Will Learn:
- Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
- Use Python to visualize data spread across multiple dimensions and extract useful features
- Dive deep into the world of analytics to predict situations correctly
- Implement machine learning classification and regression algorithms from scratch in Python
- Be amazed to see the algorithms in action
- Evaluate the performance of a machine learning model and optimize it
- Solve interesting real-world problems using machine learning and Python as the journey unfolds
Who this book is for:
This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed.
Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and second edition of Python Machine Learning by Example. He is an experienced machine learning scientist focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and applied his ML expertise in computational advertising, where he developed ad bidding and targeting algorithms based on Reinforcement Learning techniques. He published five first-authored IEEE transaction and conference papers during his master's research in University of Toronto. He is currently a Software Engineer, Machine Learning at Google.
