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Machine Learning – Hands On Python & R In Data Science

Are you Interested in the field of Machine Learning? Then this online course is for you!

This online course has been designed by two professional Data Scientists(Kirill Eremenko and Hadelin de Ponteves) so that we can let you know  our knowledge and help you to learn complex theory, algorithms and coding libraries in a very simple and easiest way.

We will help you step-by-step into the World of Machine Learning. With every and each tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This online course is very easy and exciting to learn , but at the same time we dive deep into Machine Learning. It is structured the following ways below:

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

Moreover, this course is full of  practical exercises which are based on live examples. So not only will you can learn the theory very well , but you can also get some hands-on practice building your own models.

This course includes both Python and R code templates as a bonus which you can download and use on your own projects.

If you need more details about this course, visit here-> View Here

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