Python Machine Learning By Example - Second Edition
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.
Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.
With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.
By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
|Course Length||11 hours 27 minutes|
|Date Of Publication||28 Feb 2019|
|Defining machine learning and why we need it|
|A very high-level overview of machine learning technology|
|Core of machine learning – generalizing with data|
|Preprocessing, exploration, and feature engineering|
|Installing software and setting up|
|Finding separating boundary with support vector machines|
|Classifying newsgroup topics with SVMs|
|More example – fetal state classification on cardiotocography|
|A further example – breast cancer classification using SVM with TensorFlow|
|Brief overview of advertising click-through prediction|
|Getting started with two types of data – numerical and categorical|
|Exploring decision tree from root to leaves|
|Implementing a decision tree from scratch|
|Predicting ad click-through with decision tree|
|Ensembling decision trees – random forest|
|Converting categorical features to numerical – one-hot encoding and ordinal encoding|
|Classifying data with logistic regression|
|Training a logistic regression model|
|Training on large datasets with online learning|
|Handling multiclass classification|
|Implementing logistic regression using TensorFlow|
|Feature selection using random forest|