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Machine Learning A-Z™: Python & R in Data Science [2023]

via Udemy


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 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 900,000 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 isfun 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,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

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-lifecase studies. So not only will you learn the theory, but you will also get lots of 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.

Taught by

Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team and SuperDataScience Support


4.5 rating at Udemy based on 166252 ratings

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