Machine Learning -Hands On Python and R In Data Science
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists (Kirill Eremenko and Hadelin de Ponteves)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 which are based on live 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.
If you need more details about this course, visit here-> View Here