Introduction to Machine Learning with Python: A Guide for Beginners in Data Science

Posted By: AlenMiler
Introduction to Machine Learning with Python: A Guide for Beginners in Data Science

Introduction to Machine Learning with Python: A Guide for Beginners in Data Science by Peters Morgan
English | 26 July 2018 | ASIN: B07FYB57KW | 190 Pages | EPUB | 1.95 MB

Are you thinking of learning more about Machine Learning using Python?

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 Publisher

Our 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 Users

The 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.

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