Book 1: Learn Python
How to Work with the Python Language
The Benefits of Python
What is Machine Learning?
What Can Machine Learning Be Used For?
Machine Learning vs. Artificial Intelligence
The Different Types of Machine Learning
A Look to Begin
Avoiding Error
Keep the Programmers in the Loop
What Can Machine Learning Do?
Into the Future
Chapter 7: Working with Recurrent Neural Networks
Chapter 8: Clustering Algorithms
Chapter 9: Decision Trees and Turning Them Into Random Forests
Chapter 10: The Support Vector Machines
Chapter 11: How to Work with Validation and Optimization Techniques
Techniques for Cross-Validation
The Hyperparameter Optimization
Random and Grid Search
Chapter 12: Other Algorithms That Work Well with Python Machine Learning
NaΓ―ve Bayes
Markov Algorithm
Regression Algorithms
Q-Learning
Conclusion
Introduction
Chapter 1: The Basics of Data Science
Why Does My Business Need Data Science?\
A Look at Data Science
Data Science and Business Intelligence
The Lifecycle of Data Science
Chapter 2: How Does the Python Language Fit In With Data Science?
Chapter 3: The Best Python Libraries to Help with Data Science
NumPy
SciPy
Pandas
Matplotlib
Scikit-Learn
Theano
TensorFlow
Keras
Chapter 4: Gathering Your Data
Know Your Biggest Business Problem
Places to Look for the Data
Where to Store the Data?
Chapter 5: Organizing and Cleaning the Data
What is Data Preparation?
Why Is Data Preparation So Important?
Steps Involved for Data Preparation