## Best Machine Learning courses & tutorials 2018

### 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 structed 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. This is one of the best Machine Learning Python tutorials in 2018.

### 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 2017.

### 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.

## Best Machine Learning books 2018

### Bestsellers

- O Reilly Media
- Aurélien Géron
- Publisher: O'Reilly Media
- Edition no. 1 (04/09/2017)
- Paperback: 574 pages

- Ian Goodfellow, Yoshua Bengio, Aaron Courville
- The MIT Press
- Kindle Edition
- English

- The MIT Press
- John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
- Publisher: The MIT Press
- Edition no. 1 (07/24/2015)
- Hardcover: 624 pages

- Sebastian Raschka, Vahid Mirjalili
- Publisher: Packt Publishing
- Edition no. 2 (09/20/2017)
- Paperback: 622 pages

- Oliver Theobald
- Scatterplot Press
- Kindle Edition
- Edition no. 2 (06/21/2017)
- English

- Andriy Burkov
- Publisher: Andriy Burkov
- Paperback: 158 pages

- Nick McClure
- Publisher: Packt Publishing - ebooks Account
- Paperback: 370 pages

- Andreas C. Müller, Sarah Guido
- Publisher: O'Reilly Media
- Edition no. 1 (10/21/2016)
- Paperback: 400 pages

- Julian Avila
- Publisher: Packt Publishing - ebooks Account
- Edition no. 2 (11/16/2017)
- Paperback: 374 pages

- Springer
- Christopher M. Bishop
- Publisher: Springer
- Hardcover: 738 pages

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Last updated on January 16th, 2019

Last update on 2019-01-24 / Affiliate links / Images from Amazon Product Advertising API