Learn to create Machine Learning Algorithms in Python and R from two Data Science experts

MACHINE LEARNING A-Z: HANDS-ON PYTHON & R IN DATA SCIENCE

Download this udemy course and Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

What you’ll learn from this course:

  • You will be able to Master Machine Learning on Python & R
  • You will Have a great intuition of many Machine Learning models
  • You will be able to Make accurate predictions
  • You will be able to Make powerful analysis
  • You will be able Make robust Machine Learning models by yourself.
  • have strong added value to your business
  • how to Use Machine Learning for personal purpose
  • how to Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • you will Handle advanced techniques like Dimensionality Reduction
  • you will Know which Machine Learning model to choose for each type of problem
  • you build an army of powerful Machine Learning models and know how to combine them to solve any problem.

 

Requirements for this course:

Before taking this course make sure you;

  • have high school mathematics level.

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Description

If you are 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 they can share their knowledge and help you learn complex theory, algorithms and coding libraries in a more simpler way.

This course will take 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:

 

 

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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 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.

 

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Who this course 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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Curriculum For This Course

287 Lectures

41:12:35

Course downloadable Size: 6.84G

Official course:https://www.udemy.com/machinelearning/

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