Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python
- Length: 128 pages
- Edition: 1
- Language: English
- Publisher: Independently published
- Publication Date: 2021-08-25
- ISBN-10: B09DMRGX1W
- ISBN-13: 9798463520876
- Sales Rank: #0 (See Top 100 Books)
Ready to discover the Machine Learning world?
Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do?
Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data.
Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks.
By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics.
The Topics Covered Include:
- Machine learning fundamentals
- How to set up the development environment
- How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy
- How to explore data
- How to solve regression and classification problems
- Decision trees
- k-means clustering
- Feed-forward and recurrent neural networks
Get your copy now
Ready to discover the Machine Learning world?
Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do?
Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data.
Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks.
By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics.
The Topics Covered Include:
- Machine learning fundamentals
- How to set up the development environment
- How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy
- How to explore data
- How to solve regression and classification problems
- Decision trees
- k-means clustering
- Feed-forward and recurrent neural networks
About this Book Note on Code and Datasets Requirements Chapter 1: Understanding Machine Learning What is Machine Learning? Supervised Learning Semi-Supervised Learning Unsupervised Learning Reinforcement Learning Challenges Bad Data Missing Data Chapter 2: Getting Started with Python Machine Learning with Python Python Installation Guide Installing Python Scientific Distributions Anaconda ActivePython Canopy WinPython CPython Python Machine Learning Packages and Tools Scikit-learn NumPy SciPy Matplotlib Pandas Jupyter Notebooks TensorFlow Working in Virtual Environments Datasets Your First Machine Learning Model The Iris Dataset The Train/Test Split Data Assessment Machine Learning with k-Nearest Neighbors Machine Learning Model Evaluation Chapter 3: Supervised Machine Learning Classification and Regression Types Overfitting, Underfitting, and Generalization Amount of Data vs. Model Complexity Supervised Learning Algorithms Datasets, Datasets, Datasets Assignment K-Nearest Neighbors Classification Linear Regression Models Linear Regression (The Classic) Assignment Ridge Regression Assignment Linear Classification Models Assignment Notes on Linear Model Parameters The Naive Bayes Classifiers Notes on Naive Bayes Parameters Assignment Decision Trees Pruning Creating Decision Trees Assignment Note on Creating your own Features Automatic Creation Chapter 4: Unsupervised Machine Learning Unsupervised Learning Categories The Challenge Transformations Principal Component Analysis PCA Application K-Means Clustering Chapter 5: Neural Networks Theory and Deep Learning The Concept of Neural Networks Basic Neural Network Architecture Feed-Forward Neural Networks Gaining a Deeper Understanding Backpropagation Neural Networks and Overfitting Recurrent Neural Networks The Restricted Boltzmann Machine Chapter 6: Give Your Machine Learning Models a Boost Learning Curves Cross-Validation Finding the Best Hyper-Parameters Finding the Best Error Metric Working with Multiple Models Model Averaging Model Stacking Feature Engineering Feature Selection More Data FAQ Conclusion Other Books from the Author
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