Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras
Key Features
Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras
Implement advanced concepts and popular machine learning algorithms in real-world projects
Build analytics, computer vision, and neural network projects
Book DescriptionMachine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.
The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.
By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects.
What you will learn
Understand the Python data science stack and commonly used algorithms
Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
Understand NLP concepts by creating a custom news feed
Create applications that will recommend GitHub repositories based on ones you've starred, watched, or forked
Gain the skills to build a chatbot from scratch using PySpark
Develop a market-prediction app using stock data
Delve into advanced concepts such as computer vision, neural networks, and deep learning
Who this book is forThis book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.
Key Features
Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras
Implement advanced concepts and popular machine learning algorithms in real-world projects
Build analytics, computer vision, and neural network projects
Book DescriptionMachine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.
The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.
By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects.
What you will learn
Understand the Python data science stack and commonly used algorithms
Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
Understand NLP concepts by creating a custom news feed
Create applications that will recommend GitHub repositories based on ones you've starred, watched, or forked
Gain the skills to build a chatbot from scratch using PySpark
Develop a market-prediction app using stock data
Delve into advanced concepts such as computer vision, neural networks, and deep learning
Who this book is forThis book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.
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