Repository For Codes And Concept Taught in Udemy Course

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• 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

• Recommender System
  • Similar Movies
  • Item Based Collabrative Filtering

• Tensorflow
  • Keras-RNN
  • Keras-CNN

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

• 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

Ashlesh Khajbage

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

1. Course Reference Thumbnail

Udemy

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3. Certificate

4. I am Extremely ThankFull For

Course Link -> Udemy

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