Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow: Step-by-Step Tutorial For Beginners.

• A simple language has been used.
• Many examples have been given, both theoretically and programmatically.
• Screenshots showing program outputs have been added.

• To help you understand the basics of machine learning and deep learning.
• Understand the various categoriesof machine learning algorithms.
• To help you understand how different machine learning algorithms work.
• You will learn how to implement various machine learning algorithms programmatically in Python.
• To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.
• To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.

• Anybody who is a complete beginner to machine learning in Python.
• Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.
• Professionals in data science.
• Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.
• Students and academicians, especially those focusing on neural networks, machine learning, and deep learning.

• Python 3.X
• Numpy
• Pandas
• Matplotlib

The Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning.

• Getting Started
• Environment Setup
• Using Scikit-Learn
• Linear Regression with Scikit-Learn
• k-Nearest Neighbors Algorithm
• K-Means Clustering
• Support Vector Machines
• Neural Networks with Scikit-learn
• Random Forest Algorithm
• Using TensorFlow
• Recurrent Neural Networks with TensorFlow
• Linear Classifier