Introduction to Machine Learning with Python: A Guide for Beginners in Data Science
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Are you thinking of learning more about Machine Learning using Python? (For Beginners)This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions.
From AI Sciences PublisherOur books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.
Step By Step Guide and Visual Illustrations and ExamplesThis book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications.
Target UsersThe book designed for a variety of target audiences. The most suitable users would include:
- Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field.
- Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.
- Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current techniques and approaches.
What’s Inside This Book?
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Semi-supervised Learning Algorithms
- Reinforcement Learning Algorithms
- Overfitting and underfitting
- The Bias-Variance Trade-off
- Feature Extraction and Selection
- A Regression Example: Predicting Boston Housing Prices
- Import Libraries:
- How to forecast and Predict
- Popular Classification Algorithms
- Introduction to K Nearest Neighbors
- Introduction to Support Vector Machine
- Example of Clustering
- Running K-means with Scikit-Learn
- Introduction to Deep Learning using TensorFlow
- Deep Learning Compared to Other Machine Learning Approaches
- Applications of Deep Learning
- How to run the Neural Network using TensorFlow
- Cases of Study with Real Data
- Sources & References