Machine Learning AZ: Practical Python and R in Data Science- MOOC- Udemy verified listing

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 theories, algorithms, and coding libraries in an easy way.

We will guide you step by step in the world of machine learning.

With each tutorial, you'll develop new skills and improve your understanding of this challenging but lucrative subfield of data science.

This course is fun and exciting, but at the same time, we delve into machine learning.

It is structured as follows: 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 - Learning by reinforcement: Upper Confidence Limit, Thompson Sampling Part 7 - Natural Language Processing: Bag-of-Words Model Algorithms for NLP Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 - Dimensionality Reduction: PCA, LDA, Core PC Part 10 - Model Selection and Strengthening: k-fold cross-validation, parameter tuning, grid search, XGBoost Also, the cu rso is packed with practical exercises based on real-life examples.

So you'll not only learn the theory, but also get some practice building your own models.

And as a bonus, this course includes Python and Rcode templates that you can download and use in your own projects.

Major Updates (June 2020): CODES ALL UP TO DATE EEP LEARNING ENCODED ON TENSORFLOW .0 TOP GRADIENT BOOSTING MODELS, INCLUDING XGBOOST AND EVEN CATBOOST.