Python Machine Learning Projects
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Python Machine Learning Projects
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.
Python Reinforcement Learning Projects
Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook Description Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life. What you will learnTrain and evaluate neural networks built using TensorFlow for RLUse RL algorithms in Python and TensorFlow to solve CartPole balancingCreate deep reinforcement learning algorithms to play Atari gamesDeploy RL algorithms using OpenAI UniverseDevelop an agent to chat with humans Implement basic actor-critic algorithms for continuous controlApply advanced deep RL algorithms to games such as MinecraftAutogenerate an image classifier using RLWho this book is for Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.
Python Machine Learning Projects
Author: Dr. Deepali R Vora
language: en
Publisher: BPB Publications
Release Date: 2023-03-13
A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques KEY FEATURES ● Understand the core concepts and algorithms of Machine Learning. ● Get started with your Machine Learning career with this easy-to-understand guide. ● Discover different Machine Learning use cases across different domains. DESCRIPTION Since the last two decades, there have been many advancements in the field of Machine Learning. If you are new or want a comprehensive understanding of Machine Learning, then this book is for you. The book starts by explaining how important Machine Learning is today and the technology required to make it work. The book then helps you get familiar with basic concepts that underlie Machine Learning, including basic Python Programming. It explains different types of Machine Learning algorithms and how they can be applied in various domains like Recommendation Systems, Text Analysis and Mining, Image Processing, and Social Media Analytics. Towards the end, the book briefly introduces you to the most popular metaheuristic algorithms for optimization. By the end of the book, you will develop the skills to use Machine Learning effectively in various application domains. WHAT YOU WILL LEARN ● Discover various applications of Machine Learning in social media. ● Explore image processing techniques that can be used in Machine Learning. ● Learn how to use text mining to extract valuable insights from text data. ● Learn how to measure the performance of Machine Learning algorithms. ● Get familiar with the optimization algorithms in Machine Learning. WHO THIS BOOK IS FOR This book delivers an excellent introduction to Machine Learning for beginners with no prior knowledge of coding, maths, or statistics. It is also helpful for existing and aspiring data professionals, students, and anyone who wishes to expand their Machine Learning knowledge. TABLE OF CONTENTS 1. Introduction to ML 2. Python Basics for ML 3. An Overview of ML Algorithms 4. Case Studies and Projects in Machine Learning 5. Optimization in ML Algorithms