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    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
    Statistics
    Probability
    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)
    Pandas
    Scientific Python (Scipy)
    Matplotlib
    Scikit-Learn
    K-Nearest Neighbors
    Naive Bayes
    Simple and Multiple Linear Regression
    Logistic Regression
    Generalized Linear Models
    Decision Trees and Random Forest
    Neural Networks
    Perceptrons
    Backpropagation
    Clustering
    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