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Section 4: Simple Linear Regression
Section 5: Multiple Linear Regression
Section 6: Polynomial Regression
Section 11: Regression Model Selection in Python
Section 12: Regression Model Selection in R
Section 13: Logistic Regression
Section 20: Classification Model Selection in Python
Section 22: K-Means Clustering
Section 23: Hierarchical Clustering
Section 26: Upper Confidence Bound (UCB)
Section 27: Thompson Sampling
Section 28: -------------------- Part 7: Natural Language Processing --------------------
Section 29: -------------------- Part 8: Deep Learning --------------------
Section 30: Artificial Neural Networks
Section 31: Convolutional Neural Networks
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts.Code templates included.
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:
Moreover,the course is packed with practical exercises which 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.
Who this course is for: