Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
What you'll learn:
Master Machine Learning on Python & R
Have a great intuition of many Machine Learning models
Make accurate predictions
Make powerful analysis
Make robust Machine Learning models
Create strong added value to your business
Use Machine Learning for personal purpose
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning model to choose for each type of problem
Build an army of powerful Machine Learning models and know how to combine them to solve any problem
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 isfun 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,PolynomialRegression,SVR, Decision Tree Regression,Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification,RandomForest 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 modelandalgorithms 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-lifeexamples. 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 bothPython and Rcode 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!
Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team and SuperDataScience Support