4.51 out of 5
4.51
143972 reviews on Udemy

მანქანური სწავლება A-Z ™: პრაქტიკა – პითონზე და R-ზე მონაცემთა მეცნიერებაში – English

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Instructor:
Kirill Eremenko
760,689 students enrolled
English [Auto] More
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 is fun 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, 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

Moreover, the course is packed with practical exercises that 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.

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!

 

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.5
4.5 out of 5
143972 Ratings

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Includes

44 hours on-demand video
75 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion
გამოიწერე ჩვენი ტრენინგები და მიიღე 40% ფასდაკლება, ასევე უფასო მასტერკლასები

 

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