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

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Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code template included

Machine-Learning-A-Z™-Hands-On-Python-R-In-Data-Science

Machine Learning Course

What Will I Learn in the course?

  • Master of Machine Learning on Python & R
  • Have a great intuition of Machine Learning models
  • Make an accurate prediction.
  • powerful analysis for machine
  • Make a robust Machine Learning model.
  • for your business Create strong added values
  • Use Machine Learning for personal purpose, personal use,
  • Handle specific topics like Reinforcement Learning and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which ML model to choose for each other type of problem
  • Build an army of powerful Machine Learning models and know-how to combine them to solve problems.

Requirements

Just some high school mathematics level

Description

Interested in the field of ML?

This course has been designed by two professional Data Scientists who can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you one-by-one into the World of ML. With every video, you will develop and update your new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is exciting and learns Knowledge, deep into Machine Learnings.

  1. Data Preprocessing
  2. Regression as a Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression as a may type of Regression
  3. Classification: Logistic Regression, K-NN, S-V-M, Kernel SVM, Naive Bayes, Random Forest Classification, Decision Tree Classification
  4. Clustering: K-Means, Hierarchical Clustering
  5. Apriori, Eclat Association Rule Learning
  6. Reinforcement Learning: Upper Confidence Bound and Thompson Sampling
  7. Natural Language Processing algorithms for NLP and: Bag-of-words model.
  8. Deep Learning: Artificial Neural Networks, Convolutional Networks
  9. Dimensionality Reduction: PCA and Kernel PCA
  10. Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XG Boost

Moreover, the course is packed with practical exercises which are based on live and practical examples to your easily understand. So not only will you learn the theory, but you will also get some hands-on practice building your own models. and one-by-one explain

And as a bonus, this course includes both Python and R code templates which you can download and use on your project.and grow your knowledge

Who is the target audience?

  • Anyone interested in Machine Learning
  • Students school knowledge in math and want to start learning Machine Learning.
  • people know the basics of Ml, want to learn more knowledge.
  • including classical algorithms like linear regression or logistic regression.
  • Any people who are not comfortable but interested in Machine Learning want to apply it easily on datasets. And all student are easily to understand this udemy free course.
  • Any students in college who want to start a career, its a best option is Data Science.
  • data analysts want to Uplevel.

Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team

  • Created by Kirill Eremenko, Hadelin de Ponteves,
  • SuperDataScience Team
  • English

DOWNLOAD More Course…Click

https://www.udemy.com/course/machinelearning/https://www.googleadservices.com/pagead/aclk?sa=L&ai=DChcSEwi5idDVy_3nAhXZDSsKHaHpBaEYABAAGgJzZg&ohost=www.google.com&cid=CAESQOD2z6WZN7c-tX3shBFocigeyxtHybA3Bb4Y49SmaLb9RrFnmcYFU9b4hj7gnb-BK7rdiZ0MS_JfKRjMK6XfulY&sig=AOD64_0eMnb-ynoZX1F9VV1VDby2eaU1JA&q=&ved=2ahUKEwjh3MbVy_3nAhWeyjgGHU6bBeMQ0Qx6BAhnEAE&adurl=

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