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Machine Learning A-Z™: Hands-On Python & R In Data Science
Learn to create Machine Learning Algorithms in Python and R from two Data
Science experts. Code templates included.
Science experts. Code templates included.
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Support, Ligency Team
- Last updated 4/2021
- English
- English [Auto], French [Auto] , German [Auto], Italian [Auto], Portuguese [Auto], Spanish [Auto]
- Master Machine Learning on Python & R
- Make accurate predictions
- Make robust Machine Learning models
- Use Machine Learning for personal purpose
- Handle advanced techniques like Dimensionality Reduction
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
- Have a great intuition of many Machine Learning models
- Make powerful analysis
- Create strong added value to your business
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Know which Machine Learning model to choose for each type of problem
30-Day Money-Back Guarantee
- 44 hours on-demand video
- 75 articles
- 38 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Get your team access to 5,500+ top Udemy courses anytime, anywhere.
Curated for the Udemy for Business collection
- Just some high school mathematics level.
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured 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
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Important updates (June 2020):
- CODES ALL UP TO DATE
- DEEP LEARNING CODED IN TENSORFLOW 2.0
- TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!