Python Machine Learning for Beginners Machine Learning (ML) and Artificial Intelligence (AI) have firmly established their presence. Based on substantial data and evidence, it’s clear that ML and AI are not just passing trends but integral to the future. Across various industries, the practical applications of ML are driving significant business outcomes. From healthcare, e-commerce, government, and transportation to social media, financial services, manufacturing, oil and gas, marketing, and sales, ML is making a substantial impact.
So, what exactly does a Machine Learning professional do? They specialize in developing intelligent algorithms that learn and adapt quickly from data, making precise predictions. Python Machine Learning for Beginners takes a hands-on approach to expedite your ML learning process.
What sets this book apart? AI Publishing strongly advocates the “learning by doing” methodology, and this book is meticulously crafted with that principle in mind. Emphasizing both theoretical understanding and practical application, the book is divided into two halves.
In the first half, delve into data analysis and visualization in detail. The second half explores machine learning and statistical models for data science. Each chapter provides the theoretical framework behind various data science and machine learning techniques, complemented by practical examples illustrating their applications.
Upon purchasing this book, you gain access to a wealth of related learning materials available on the publisher’s website at no extra cost. This includes references, PDFs, Python codes, and exercises. ML datasets used in the book can be downloaded at runtime or accessed through the Resources/Datasets folder. The second chapter even offers a short course on Python programming, making it incredibly useful for those new to Python. With the Python codes and datasets readily available, all you need is a computer with internet access to kickstart your learning journey.
Key topics covered include:
- Introduction and Environment Setup
- Python Crash Course
- Python NumPy Library for Data Analysis
- Introduction to Pandas Library for Data Analysis
- Data Visualization via Matplotlib, Seaborn, and Pandas Libraries
- Solving Regression Problems in ML Using Sklearn Library
- Solving Classification Problems in ML Using Sklearn Library
- Data Clustering with ML Using Sklearn Library
- Deep Learning with Python TensorFlow 2.0
- Dimensionality Reduction with PCA and LDA Using Sklearn
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See also
- Software Exorcism: A Handbook for Debugging and Optimizing Legacy Code by Bill Blunden (2003)
- Hormones and the Endocrine System: Textbook of Endocrinology by Bernhard Kleine (2016)
- Genetics: A Conceptual Approach 6e by Benjamin A. Pierce (2017)
- A Functional Approach to Java by Ben Weidig (2022)
- Calculus of variations and optimal control by Amol Sasan (2005)