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Hands On Machine Learning with Python: Concepts and Applications for Beginners
Are you thinking of learning more about Machine Learning using Python? (For Beginners)
This book is for you. It would seek to explain you all need to know about machine learning and its application using Python in an intuitive way.
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.
What’s Inside This Book?
< 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
Frequently Asked Questions
Q: Is this book for me and do I need programming experience?
A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.
Q: Does this book include everything I need to become a Machine Learning expert?
A: Unfortunately, no.
This book is for you. It would seek to explain you all need to know about machine learning and its application using Python in an intuitive way.
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.
What’s Inside This Book?
< 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
Frequently Asked Questions
Q: Is this book for me and do I need programming experience?
A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.
Q: Does this book include everything I need to become a Machine Learning expert?
A: Unfortunately, no.
225 pages, Kindle Edition
Published August 1, 2018
65 people are currently reading
22 people want to read
About the author
John Anderson
697 books11 followersThere is more than one author with this name in the Goodreads' database
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Displaying 1 of 1 review
July 19, 2019
A good overview of common machine learning techniques in python, but when its gets into the deep learning examples does not do a great job explaining.
Displaying 1 of 1 review