Hands On Machine Learning with Python
***** BUY NOW (will soon return to 25.89 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****
>****** Free eBook for customers who purchase the print book from Amazon ******
Are you thinking of learning more about Machine Learning using Python? (For beginners)This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning.
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.
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?<
- Overview of Python Programming Language
- The Data Science Process
- Machine Learning
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Semi-supervised Learning Algorithms
- Reinforcement Learning Algorithms
- Overfitting and Underfitting
- Python Data Science Tools
- Jupyter Notebook
- Numerical Python (Numpy)
- Scientific Python (Scipy)
- K-Nearest Neighbors
- Naive Bayes
- Simple and Multiple Linear Regression
- Logistic Regression
- Generalized Linear Models
- Decision Trees and Random Forest
- Neural Networks
- K-means with Scikit-Learn
- Bottom-up Hierarchical Clustering
- K-means Clustering
- Network Analysis
- Betweenness centrality
- Eigenvector Centrality
- Recommender Systems
- Multi-Class Classification
- Popular Classification Algorithms
- Support Vector Machine
- Deep Learning using TensorFlow
- Deep Learning Case Studies