VIP Machine Learning Course

Welcome to the VIP Machine Learning Course repository! This repository contains Jupyter Notebooks covering various topics in machine learning, curated by Elliot One. Please note that while this course includes original content by Elliot One, it also integrates insights and examples from diverse resources for comprehensive learning.

ML Course Content

• Session 01

  • Median, Mean, Variance, Covariance
  • Minitab software introduction
  • Design Of Experiment (DOE)
  • Response Surface Method (RSM)
  • Box-Behnken
  • Review/Implement "Statistical modeling and optimization of AL/CNT composite using response surface-desirability approach"
  • Review/Implement "Mechanical properties of aluminum/SiNT nanocomposite"

• Session 02

  All the below topics were investigated on Titanic dataset.

  • Numpy
    • What is Numpy?
    • Numpy arrays
    • Special arrays in Numpy
    • Operations on Numpy arrays
    • Adding/Removing/Sorting elements in Numpy arrays
  • Pandas
    • Read/Update data
    • Normalize data
    • Investigate queries on data
    • Sort/subset dataframe
    • Selection and indexing (loc, iloc)
  • Matplotlib & Seaborn
    • Visualize data distribution
    • Set themes and colors
  • Chart types
    • Scatter plots
    • Box plots
    • Catplot
    • Bar plots
    • Relplot
    • Line plots
    • Regplot
    • Facetgrid

• Session 03

  All the below topics were investigated on Google Play Store Apps dataset.

  • Exploratory Data Analysis (EDA)
  • Data Preprocessing
  • Storytelling - Visualization

• Session 04

  All the below topics were investigated on Seaborn Tips dataset and Boston house price dataset.

  • Introduction to Supervised, Unsupervised and Reinforcement learning
  • Difference between regression and classification problems
  • ML/AI applications
  • Linear regression (simple and multiple)
  • Boston house price prediction project
  • Model evaluation: R2 score, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE)
  • Polynomial regression (Quadratic and cubic polynomials)

• Session 05

All the below topics were investigated on Iris Species dataset. - Hyperparameters - Gradient Descent - Logistic regression - Sigmoid function - Model evaluation: Confusion matrix, Accuracy, Recall, Precision, F1-score - Data Preprocessing: Missing values, Encoding, Feature scaling (Normalization and Standardization)

• Session 06

  All the below topics were investigated on Sklearn Breast cancer wisconsin dataset and Social Network Ads dataset.

  • Cross-validation
  • K-fold cross-validation
  • K-nearest neighbor (KNN)

• Session 07

  All the below topics were investigated on Advertising dataset and Social Network Ads dataset.

  • Regularization (L1-norm/Lasso and L2-norm/Ridge)
  • Overfitting and underfitting
  • High bias and high variance models
  • Naive Bayes

• Session 08

  All the below topics were investigated on Mobile Price dataset and Position Salaries dataset.

  • Support vector machines
  • SVC and SVR

• Session 09

  All the below topics were investigated on Churn Modelling dataset, Iris Species dataset and Digit Recognizer dataset.

  • Introduction to deep learning
  • Artificial Neural Network (ANN)
  • Multilayer perceptron (MLP)
  • Principal Component Analysis (PCA) (for Dimensionality reduction)

• Session 10

  All the below topics were investigated on Social Network Ads dataset, Position Salaries dataset and Loan dataset.

  • Decision tree
  • Information gain and gini index
  • Visualize decision tree
  • Ensemble and bagging
  • Random Forest

• Session 11

  All the below topics were investigated on Diabetes dataset, CO2 Emission dataset and Mall Customers dataset.

  • Gradient boosting
  • XGBoost
  • Ensemble and boosting
  • K-means clustering (Unsupervised learning)
  • K-means elbow method

• Session 12

  • 1.Bike Sharing Demand(Regression) (browse dataset)(project)
  • 2.Titanic(Classification) (browse dataset)(project)
  • 3.Market-Basket-Analysis(Clustering) (browse dataset)(project)
  • 4.Covid-Vaccination(Regression) (browse dataset)(project)
  • 5.Churn Modeling(Classification) (browse dataset)(project)

• Session 13

  • International Conference Paper Implementation Sample 1
  • International Conference Paper Implementation Sample 2
  • ISI Article Implementation Sample 1
  • ISI Article Implementation Sample 2

• Session 14

  • 1.Bonus Project: Coronavirus Tweets(Classification + NLP) (browse dataset)(project)
  • 2.Bonus Project: Handwrite Digit Recognition(Classification + CNN) (project)

Python Course Content

• Session 1

  • Variables
  • Expressions and statements
  • Booleans
  • Data Types
  • Conditions (Control program flow)
  • Switch statement (Python 3.10 and newer versions)

• Session 2

  • Functions
    • Built-in functions
    • Defining your own function (User functions)
  • Program flow control (Loops)
    • For loop
    • Break statement
    • Continue statement
    • While loop
  • Libraries
    • Random
    • Lambda expressions (Lambda functions)

• Session 3

  • Strings
    • Slicing
  • Regular expressions
  • Data structures
    • Sets
    • Tuples
    • Lists
    • Dictionaries
  • Transformation of data structures (Advanced)
    • Set comprehension
    • List comprehension
    • Dictionary comprehension

• Session 4

  • Files
    • Read from files
    • Save to files
    • Delete files
  • OOP (Object Oriented Programming)
  • SQL databases (MySQL)

• Session 5

  • HTTP requests
  • Web scraping