1. Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobal<\/strong><\/p>\nGreat for learning the basics of machine learning.<\/p>\n
In this book you will find:<\/p>\n
\u2022 Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data<\/p>\n
\u2022 Preparing data for analysis, including k-fold Validation<\/p>\n
\u2022 Regression analysis to create trend lines<\/p>\n
\u2022 Clustering, including k-means clustering, to find new relationships<\/p>\n
\u2022 The basics of Neural Networks<\/p>\n
\u2022 Bias\/Variance to improve your machine learning model<\/p>\n
\u2022 How to build your first Machine Learning Model to predict house values using Python<\/p>\n
2. The Hundred-Page Machine Learning Book by Andriy Burkov<\/strong><\/p>\nA compact and informative book that covers important principles, methods and approaches of machine learning.<\/p>\n
In this book you will find:<\/p>\n
\n- Supervised and unsupervised learning,<\/li>\n
- support vector machines,<\/li>\n
- neural networks<\/li>\n
- ensemble methods<\/li>\n
- gradient descent,<\/li>\n
- cluster analysis and dimensionality reduction<\/li>\n
- autoencoders and transfer learning<\/li>\n
- feature engineering and hyperparameter tuning.<\/li>\n<\/ul>\n
3. Python Machine Learning by Sebastian Raschka <\/strong><\/p>\nIt will help you to delve into the industry through a successful combination of theory and practice.<\/p>\n
In this book you will find:<\/p>\n
\n- Understand the key frameworks in data science, machine learning, and deep learning<\/li>\n
- Harness the power of the latest Python open source libraries in machine learning<\/li>\n
- Explore machine learning techniques using challenging real-world data<\/li>\n
- Master deep neural network implementation using the TensorFlow 1.x library<\/li>\n
- Learn the mechanics of classification algorithms to implement the best tool for the job<\/li>\n
- Predict continuous target outcomes using regression analysis<\/li>\n<\/ul>\n
4. Machine Learning in Action by Peter Harrington<\/strong><\/p>\nA more in-depth book with a good combination of theory and practical examples.<\/p>\n
In this book you will find:<\/p>\n
\n- Examples showing common ML tasks<\/li>\n
- Everyday data analysis<\/li>\n
- Implementing classic algorithms like Apriori and Adaboos<\/li>\n<\/ul>\n
5. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville <\/strong><\/p>\nA fundamental and widely recommended book on neural networks.<\/p>\n
In this book you will find:<\/p>\n
\n- Applied Math and Machine Learning Basics<\/li>\n
- Modern Practical Deep Networks<\/li>\n
- Sequence Modeling: Recurrent and Recursive Nets<\/li>\n
- Monte Carlo Methods<\/li>\n<\/ul>\n
If English is an obstacle for you, some of this material can be found in translation, but we strongly advise you to study English in addition to data science, because it will give you a number of benefits: from access to a much wider choice of materials to employment and work.<\/p>\n
<\/p>","protected":false},"excerpt":{"rendered":"
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