Machine Learning Books that beginners should focus on

By ridhigrg |Email | Nov 25, 2019 | 1989 Views

Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning from Scratch) Paperback - 1 Jan 2018
by Oliver Theobald
Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

This major new edition features many topics not covered in the First Edition, including Cross-Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.

In this step-by-step guide you will learn:
  • How to download free datasets
  • What tools and machine learning libraries you need
  • Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
  • Preparing data for analysis, including k-fold Validation
  • Regression analysis to create trend lines
  • Clustering, including k-means and k-nearest Neighbors
  • The basics of Neural Networks
  • Bias/Variance to improve your machine learning model
  • Decision Trees to decode classification
  • How to build your first Machine Learning Model to predict house values using Python

Machine Learning for Beginners 2019: The Ultimate Guide to Artificial Intelligence, Neural Networks, and Predictive Modelling (Data Mining Algorithms & Applications for Finance, Business & Marketing) Paperback - Import, 5 Jun 2019
by Matt Henderson
You've heard it before. The rise of artificial intelligence and how it will soon replace human beings and take away our jobs. What exactly is it capable of and how does this impact me? The real question you should be asking yourself is how can I use this to my advantage? How can I use machine learning to benefit my business and surpass my business goals? This book has the answer.

Designed for the tech novice, this book will break down the fundamentals of machine learning and what it truly means. You will learn to leverage neural networks, predictive modeling, and data mining algorithms, illustrated with real-world applications for finance, business, and marketing.

In Machine Learning for Beginners 2019, we will reveal:
  • The fundamentals of machine learning.
  • Each of the buzzwords defined!
  • 20 real-world applications of machine learning.
  • How to predict when a customer is about to churn (and prevent it from happening).
  • How to "upsell" to your customers and close more sales.
  • How to deal with missing data or poor data.
  • Where to find free datasets and libraries.
  • Exactly which machine learning libraries you need.

Machine Learning using Python Paperback - 2019
by U Dinesh Kumar Manaranjan Pradhan
This book is written to provide a strong foundation in machine learning using Python libraries by providing real-life case studies and examples. It covers topics such as foundations of machine learning, introduction to Python, descriptive analytics and predictive analytics. Advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide a step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.

Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems Paperback - 2017
by AurĂ?lien GĂ?ron
You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. with exercises in each chapter to help you apply what youĂ­ve learned, all you need is programming experience to get started.
  • Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Source: HOB