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## Best Machine Learning tutorials 2020

### Machine Learning A-Z: Hands-On Python & R In Data Science

Machine Learning A-Z: Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves and SuperDataScience Team will teach you Machine Learning using Python & R. This course has been designed by two professional Data Scientists. With over 300,000 students and an average rating of 4.5 on Udemy, this is quite simply one of the best Machine Learning & Python courses. If that wasn’t enough, this course has a length of over 40 hours of video content! This makes it one of the most comprehensive Machine Learning courses ever.

The Machine Learning Python course is structured in the following way:

- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

This Python tutorial will teach you everything related to Machine Learning, step-by-step. You will build an army of powerful Machine Learning models. Then you will combine them to solve any problem. You will be able to handle different topics like Reinforcement Learning, NLP and Deep Learning. Advanced techniques like Dimensionality Reduction are also taught.Using the knowledge you gain, you will know which **Machine Learning model** to use depending on the problem. **Learn Machine Learning** from the **best Machine Learning tutorial** in 2020.

### Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp by Jose Portilla will teach you how to use Python for Data Science and Machine Learning. You will use different Python frameworks and libraries such as NumPy, Pandas, Seaborn, Matplotlib, Scikit-Learn, Tensorflow and more. This Python tutorial will show you how to use Python to implement Machine Learning algorithms. You will use SciKit-Learn for Machine Learning. This tutorial will show you how use Matplotlib and Seaborn for data visualizations. Use Spark for Big Data analysis. You will understand what Natural Language Processing is along with Spam Filters. K Nearest Neighbors and K Means Clustering are discussed. You will learn all about Neural Networks. This Python data Science training will teach you how to support Vector Machines. Decision Trees and Random Forests are both explained. This is one of the best Data Science Python courses in 2020.

## Best Machine Learning courses 2020

### Data Science and Machine Learning Bootcamp with R

Data Science and Machine Learning Bootcamp with R by Jose Portilla will teach you how to use the R programming language for data science, machine learning, and data visualization. This R programming language tutorial is a comprehensive course that is almost 18 hours in length. It covers everything R programming related. You will learn how to use R to handle csv, excel, SQL files or web scraping. This R programming course will teach you how to use R for Data Science and Data Analysis. This R video course will teach you Machine Learning. Some of the Machine Learning topics you will learn include Linear Regression, K Nearest Neighbors, K Means Clustering, Decision Trees, Random Forests, etc. **Learn Machine Learning** from the **best Machine Learning course** in 2020.

## Best Machine Learning books 2020

### Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

- Géron, Aurélien (Author)
- English (Publication Language)
- 856 Pages - 10/15/2019 (Publication Date) - O'Reilly Media (Publisher)

Through a series of recent breakthroughs, deep learning has stimulated the whole field of machine learning. Now, even programmers who know almost nothing about this technology can use simple and effective tools to implement programs that can learn from data. This practical book shows you how.

Using concrete examples, a minimal theory, and two production-ready Python frameworks – Psychite-Learn and TenserFlow – author Orlean Geron helps you gain an intuitive knowledge of the concepts and tools of creating intelligent systems. You will learn a variety of techniques, including general linear regression and advances in deep neural networks. A programming experience is just what you need to get started with the practice in each chapter to help you apply what you have learned.

Explore machine learning landscapes, especially neural networks

Use Psychit-Learn to follow the example of the end-to-end machine learning project

Look for several training models including support vector machines, decision trees, random forests and aggregation methods

Use the TensorFlow Library to create and train neural networks

Immerse yourself in neural network architectures, including convincing networks, repeat networks, and deep reinforcement learning.

Learn deep neural networks training and scaling techniques

### Building Machine Learning Powered Applications: Going from Idea to Product

- Ameisen, Emmanuel (Author)
- English (Publication Language)
- 260 Pages - 02/04/2020 (Publication Date) - O'Reilly Media (Publisher)

Learn the skills to design, build, and deploy applications based on machine learning (ML). During this practical manual, you will create an example of an application controlled by ML from the initial concept to the deployed product. Data scientists, software engineers and product managers, including experienced scientists and beginners, will learn step-by-step the tools, best practices and challenges involved in creating real ML applications.

Author Emanuel Ameisen, an experienced information scientist who has led an AI training program, is presenting practical ML ideas using code snippets, images, screenshots and interviews with industry leaders. Part I taught you how to plan ML applications and measure success. The second part explains how to create a functional ML model. The third part shows how the model can be improved until it meets your original vision. The fourth section covers deployment and monitoring techniques. You are about to learn:

Set your product goals and set up a machine learning problem

Quickly build the last pipeline from your first end and acquire an initial data set

Train and evaluate your ML models and address performance barriers

Place and monitor your model in a manufacturing environment

### Introduction to Machine Learning with Python: A Guide for Data Scientists

- Müller, Andreas C. (Author)
- English (Publication Language)
- 400 Pages - 10/21/2016 (Publication Date) - O'Reilly Media (Publisher)

Machine learning has become an integral part of many commercial applications and research projects, but the region is not exclusive to large companies with large research teams. If you use Python, even as a beginner, this book will teach you practical ways to create your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

Learn the steps needed to build a successful machine learning application, including Python and the Psychite-Learn Library. Authors Andreas Mueller and Sarah Guido focus on the practicality of using machine learning algorithms rather than the math behind them. Getting acquainted (gain, obtain) with present-day libraries will help you to get the most out of this book. You will learn:

Basic concepts and applications of machine learning

Advantages and disadvantages of widely used machine learning algorithms

What aspects of data should be focused on, including how to present data processed by machine learning?

Advanced methods for model evaluation and parameter adjustment

The concept of pipelines in chain models and equipping your workflow

Methods of working with text data, including text-specific processing techniques

Advice for improving your machine learning and data science skills

### Machine Learning For Absolute Beginners: A Plain English Introduction

- Theobald, Oliver (Author)
- English (Publication Language)
- 155 Pages - 01/01/2018 (Publication Date) - Independently published (Publisher)

The second edition of Machine Learning for Absolute Beginners was created and designed for the perfect beginner. This means explanation in simple English and no coding experience required. When the basic algorithm is introduced, clear explanations and visual examples are added to make the house clear to follow and interesting.

This new edition introduces a number of issues, including cross-validation, data cleansing, and overall modeling. Please note that this book is not a continuation of the first edition, but a revised and revised version of the first edition. Readers of the first edition should not feel pressured to buy this second edition. In this step-by-step guide, you will learn:

– How to download free datasets

– The machine learning tools and library you need

– Data cleanup strategies including hot encoding, integration and missing data processing

– Prepare data for analysis with K-fold validity

– Regression analysis to create trend lines

– Clustering with K-average and nearest K-neighbor

– The basics of neural networks

– Bias / variant to improve your machine learning model

– Decide to decode the tree

– How to create your first machine learning model to predict room quality using Python

### Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition

- Bonaccorso, Giuseppe (Author)
- English (Publication Language)
- 798 Pages - 01/31/2020 (Publication Date) - Packt Publishing (Publisher)

Updated and revised the second edition of the best-selling guide to explore and master the most important algorithms for solving complex machine learning problems. Updated to include new algorithms and techniques. The code has been updated to Python 3.8 and TensorFlow 2.x new coverage of regression analysis, time series analysis, deep learning models and advanced applications.

The second version, the Mastering Machine Learning Algorithm, helps you use the true power of machine learning algorithms to implement clever ways to meet today’s irresistible data needs. This recently updated and revised guide will help you master the algorithms widely used in semi-supervised learning, empowerment learning, supervised learning and observational learning.

You will use all the modern libraries of the Python ecosystem, including Numpy and Keras, to extract functionality from various data complexities. Dyeing from the Bayesian models to the hidden Markov models from the Monte Carlo Markov chain algorithm, this machine learning book teaches you how to extract entities from your dataset, reduce complex dimensions, and create models. Supervise and semi-supervise using Python based models from libraries such as Psychit-Learn. You will learn complex techniques such as the highest probability estimates, Hibbian learning and the formation of efficient neural networks for complete learning. You will also discover practical applications for how to use X. .

Towards the end of this book, you will be able to apply end-to-end machine learning problems and use ready-to-solve and case scenarios. You will learn:

Understand the features of a machine learning algorithm

Apply algorithms from supervised, semi-supervised, monitored and RL domains

Learn how regression works in time series analysis and risk forecasting

Create, model and train complex potential models

Collect big data and evaluate the accuracy of the model

Discover how artificial neural networks work – train, adapt and validate them

Work with automatic encoders, Hebrew networks and GNS

This book is for data science professionals who want to explore complex ML algorithms to understand how to create different machine learning models. Knowledge of Python programming is required.

### Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

- Raschka, Sebastian (Author)
- English (Publication Language)
- 770 Pages - 12/12/2019 (Publication Date) - Packt Publishing (Publisher)