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

Requirements
Just some high school mathematics level.

Description
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a
Data Scientist and a Machine Learning expert
so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
Over 1 Million students
world-wide trust this course.
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 can be completed by either doing either the
Python tutorials, or R tutorials,
or both - Python & R. Pick the programming language that you need for your career.
This course is fun and exciting, and 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
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and
learn what you need for your career right now
.
Moreover, the course is packed with practical exercises that are based on
real-life case studies
. So not only will you learn the theory, but you will also get lots of
hands-on practice
building your own models.
And last but not least, this course
includes both Python and R code templates
which you can download and use on your own projects.
Who this course is for:
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning tools.

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