Machine Learning A-Z: Hands-On Python & R In Data Science
Machine Learning A-Z: Hands-On Python & R In Data Science. Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
The name of this course is Machine Learning A-Z: Hands-On Python & R In Data Science. The knowledge you will get with this indescribable online course is astonishing. Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, one of the very best experts in this field.
Description of this course: Machine Learning A-Z: Hands-On Python & R In Data Science
Course Description 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: Part 1 – Data PreprocessingPart 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 – Clustering: K-Means, Hierarchical ClusteringPart 5 – Association Rule Learning: Apriori, EclatPart 6 – Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 – Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 – Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Moreover, the course is packed with practical exercises which are based on live 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.
Requirements of this course: Machine Learning A-Z: Hands-On Python & R In Data Science
What are the requirements? Just some high school mathematics level
What will you learn in this course: Machine Learning A-Z: Hands-On Python & R In Data Science?
What am I going to get from this course? 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
Target audience of this course: Machine Learning A-Z: Hands-On Python & R In Data Science
Who is the target audience? 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 confortable 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
Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
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