/Machine-Learning-A-Z-hands-on-Python-And-R-in-data-Science

Repository For Codes And Concept Taught in Udemy Course

Primary LanguagePythonMIT LicenseMIT

Machine-Learning-A-Z-hands-on-Python-And-R-in-data-Science

Repository For Codes And Concept Taught in Udemy Course

forthebadge - forthebadge - forthebadge

Course Layout

  • Part 1 - Data Preprocessing
    • Missing Data
    • Categorical Data
    • Template For Preprocessing Data (General Steps)

  • Part 2 - Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
    • Evaluating Regression Model
    • Regularisation Methods

  • Part 3 - Classification
    • Logistic Regression
    • K-Nearest Neighbors (K-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Evaluating Classification Model

  • Part 4 - Clustering
    • K-Means Clustering
    • Hierarchical Clustering

  • Part 5 - Association Rule Learning
    • Apriori
    • Eclat

  • Part 6 - Reinforcement Learning
    • Upper Confidence Bound (UCB)
    • Thompson Sampling
    • Q-Learning

  • Part 7 - Natural Language Processing
    • Natural Language Processing
      • Decision Tree
      • Random Forest
      • Max Entropy

  • Part 8 - Deep Learning
    • Artificial Neural Networks(ANN)
    • Convolutional Neural Netwroks(CNN)
    • Recurrent Neural Networks(RNN)

  • Part 9 - Dimensionality Reduction
    • Principal Component Analysis
    • Linear Discriminant Analysis
    • kernel PCA

  • Part 10 - Model Selection & Boosting
    • Model Selection
    • XGBoost

Exploration Material

  • Recommender System
    • Similar Movies
    • Item Based Collabrative Filtering

  • Tensorflow
    • Keras-RNN
    • Keras-CNN

  • Projects
    • handwriting Recognition
    • Predict Political Party
    • Fire Detection Project

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

Author

Ashlesh Khajbage

Instructor

Image Name Designation
Kirill Eremenko Kirill Eremenko Data Scientist
Martin Jocqueviel Martin Jocqueviel Freelance data scientist
Hadelin de Ponteves Hadelin de Ponteves AI Entrepreneur

Reference Links

  1. Course Reference Thumbnail

Course Description

Udemy

  1. Provided By

Super Data Science Team

Note from Them - We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!.We are here to help you stay on the cutting edge of Data Science and Technology.

  1. Certificate

Certificate

  1. I am Extremely ThankFull For

Udemy

Course Link -> Udemy

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Copyright

Files available for redistribution and download only for educational purposes.