Python Machine Learning for Beginners: Unsupervised Learning, Clustering, and Dimensionality Reduction. Part 3

By Tom Lesley

Python Machine Learning for Beginners: Become a Machine Learning Pro with Python. A Beginner's Guide" is an all-in-one guide to get started with the exciting world of machine learning. This book covers all the fundamentals of machine learning, including regression analysis, classification algorithms, clustering, natural language processing, and deep learning, with a focus on practical applications in Python. Whether you're a complete beginner with no prior programming experience or have some experience but are new to machine learning, this book provides a comprehensive and hands-on approach to learning machine learning with Python.

 

With step-by-step instructions and plenty of real-world examples, this book will help you understand the concepts and techniques required to build and deploy machine learning models. You'll learn how to preprocess and analyze data, evaluate and optimize machine learning models, and deploy them in real-world applications. Along the way, you'll gain a solid understanding of the underlying theory and mathematics of machine learning.

 

By the end of this book, you'll be able to build and deploy machine learning models that can make predictions, classify data, cluster groups, analyze text, and much more. Whether you're looking to kickstart your career in machine learning, or just want to learn more about this exciting field, "Python Machine Learning for Beginners: Become a Machine Learning Pro with Python. A Beginner's Guide" is the perfect resource to get you started.

 

 

• Language: English
• Publisher: May Reads
• Release date: Apr 21, 2024
• ISBN: 9798224643097

Book preview

Tom Lesley

Table of Content

I. Introduction to Machine Learning

A. What is Machine Learning

B. Types of Machine Learning

C. Applications of Machine Learning

D. Overview of the Python programming language

II. Setting up the Environment

A. Installing Python and related packages

B. Introduction to Jupyter Notebook

C. Setting up the development environment

III. Understanding Data in Machine Learning

A. Data Preprocessing

B. Exploratory Data Analysis (EDA)

C. Handling Missing Values and Outliers D. Feature Engineering

IV. Regression Analysis

A. Introduction to Regression Analysis

B. Simple Linear Regression

C. Multiple Linear Regression

D. Polynomial Regression

V. Classification Algorithms

A. Introduction to Classification

B. Logistic Regression

C. K-Nearest Neighbors (KNN)

D. Decision Trees E. Random Forest F. Support Vector Machines (SVM)

VI. Clustering Algorithms

A. Introduction to Clustering

B. K-Means Clustering

C. Hierarchical Clustering

D. Density-Based Clustering (DBSCAN)

VII. Natural Language Processing (NLP)

A. Text Preprocessing

B. Text Classification

C. Sentiment Analysis D. Text Generation

VIII. Deep Learning

A. Introduction to Deep Learning

B. Neural Networks

C. Convolutional Neural Networks (CNN)

D. Recurrent Neural Networks (RNN)

IX. Model Evaluation and Optimization

A. Model Evaluation Metrics

B. Overfitting and Underfitting

C. Hyperparameter tuning

X. Deploying Machine Learning Models

A. Introduction to Model Deployment

B. Deploying a Machine Learning Model with Flask

XI. Conclusion

A. Recap of the Key Concepts

B. Future of Machine Learning

C. Next Steps for Further Learning

I. Introduction to Machine Learning

A. What is Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed to do so. It is a powerful tool for solving complex problems and making predictions in a wide range of industries, including finance, healthcare, marketing, and many others.

Machine learning algorithms can also be divided into parametric and non-parametric models. Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

The power of machine learning lies in its ability to learn from data and make predictions or decisions without being explicitly programmed. This allows organizations to automate processes and make decisions based on large amounts of data in real-time, leading to more efficient and accurate outcomes.

Machine learning is a rapidly growing field that is transforming the way we work and live. It has numerous applications across industries and is a valuable tool for solving complex problems and making predictions. This chapter provides a brief overview of what machine learning is and its different types, laying the foundation for the rest of the book and the exciting journey into the world of machine learning.

B. Types of Machine Learning

Machine learning algorithms can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning. Each category has its own unique set of algorithms and applications, and it is important to understand the differences between them in order to choose the right algorithm for a particular problem.

Supervised Learning: Supervised learning is the most commonly used type of machine learning. It involves training an algorithm on a dataset that includes input variables (features) and output variables (labels). The algorithm learns the relationship between the inputs and outputs, and then uses that relationship to make predictions on new data.

Supervised learning algorithms can be further divided into two subcategories: regression and classification.

Regression algorithms are used when the output variable is a continuous value, such as a stock price or the temperature. Common examples of regression algorithms include linear regression, logistic regression, and decision trees.

Classification algorithms are used when the output variable is a categorical value, such as a binary response (yes or no) or a label (dog or cat). Common examples of classification algorithms include k-nearest neighbors (KNN), support