Computer Programming for Beginners. 3 books in 1.

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 Naïve 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