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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.
Bestseller
5/5
4.5 (126,261 ratings)
666,779 students enrolled
Discount 96% off
What you'll learn
1. Master Machine Learning on Python u0026 R
2. Have a great intuition of many Machine Learning models
3. Make accurate predictions
4. Make powerful analysis
5. Make robust Machine Learning models
6. Create strong added value to your business
7. Use Machine Learning for personal purpose
8. Handle specific topics like Reinforcement Learning, NLP and Deep Learning
9. Handle advanced techniques like Dimensionality Reduction
10. Know which Machine Learning model to choose for each type of problem
11. Build an army of powerful Machine Learning models and know how to combine them to solve any problem
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₹12,900
96% off
Discount 96% off
This course includes
- 44 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Requirements
1. Just some high school mathematics level.
Description
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!
