README.md

IBM Machine Learning Professional Certification

Contains Solutions and Notes for the IBM Machine Learning Professional Certificate

Note : If you would like to have a deeper understanding of the concepts by understanding all the math required, have a look at Mathematics for Machine Learning and Data Science

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Course 1 : Exploratory Data Analysis for Machine Learning

• Week 1

  • Prerequisites
  • Machine Learning Workflow

• Week 2

  • Retrieving and Cleaning Data
  • [Optional Labs]
    • Reading Data Demo1
    • Reading Data Demo2
    • Data Cleaning Practice

• Week 3

  • Exploratory Data Analysis
  • Feature Engineering
  • [Optional Labs]
    • Exploratory Data Analysis Demo
    • Exploratory Data Analysis Practice
    • Feature Engineering Demo
    • Feature Engineering Practice

• Week 4

  • Hypothesis Testing
  • [Optional Labs]
    • Hypothesis Testing Demo
    • Hypothesis Testing Practice

• [Week 5]

  • [Project]

Course 2 : Supervised Machine Learning: Regression

• Week 1

  • Linear Regression
  • [Optional Lab]
    • Linear Regression Demo
    • Transformations

• Week 2

  • Training and Test Splits
  • Polynomial Regression
  • [Optional Lab]
    • Training and Test Splits Demo
    • Polynomial Regression Practice

• Week 3

  • Cross Validation
  • [Optional Lab]
    • Cross Validation Demo
    • Cross Validation Practice

• Week 4

  • Bias Variance Trade Off
  • Ridge Regression
  • Lasso Regression
  • Elastic Net
  • [Optional Lab]
    • Polynomial Featurs and Regularization Demo

• Week 5

  • Details of Regularization
  • [Optional Lab]
    • Regularization Demo
    • Regularization Practice

• [Week 6]

  • [Project]

Course 3 : Supervised Machine Learning: Classification

• Week 1

  • Logistic Regression
  • Confusion Matrix, Accuracy, Specificity, Precision and Recall
  • [Optional Lab]
    • Logistic Regression Demo
    • Logistic Regression Practice

• Week 2

  • K Nearest Neighbors
  • [Optional Lab]
    • K Nearest Neighbors Demo
    • K Nearest Neighbors Practice

• Week 3

  • Support Vector Machine
  • [Optional Lab]
    • Support Vector Machine Demo
    • Support Vector Machine Practice

• Week 4

  • Decision Trees
  • [Optional Lab]
    • Decision Trees Demo
    • Decision Trees Practice

• Week 5

  • Ensemble Based Methods
  • [Optional Lab]
    • Bagging Demo
    • Bagging Practice
    • Boosting and Stacking Demo
    • AdaBoost Practice
    • Stacking Practice
    • Gradient Boosting

• Week 6

  • Random and Synthetic Oversampling
  • [Optional Lab]
    • Model Interpretability Practice
    • Model Imbalanced Classes
    • [Project]

Course 4 : Unsupervised Machine Learning

• Week 1

  • K Means Clustering
  • [Optional Lab]
    • K Means Demo
    • K Means Clustering Practice
    • Mixture of Gaussians

• Week 2

  • Distance Metrics and Computational Hurdles
  • [Optional Lab]
    • Distance Metrics Practice
    • Curse of Dimensionality Demo

• Week 3

  • Hierarchical Agglomerative Clustering
  • [Optional Lab]
    • DBSCAN Practice
    • Mean Shift Clustering Practice
    • Clustering Methods

• Week 4

  • Dimensionality Reduction
  • [Optional Lab]
    • Matrix Review Demo
    • Dimensionality Reduction Demo
    • Principal Component Analysis Practice
    • Singular Value Decomposition Demo

• Week 5

  • Kernel Principal Component Analysis and Multidimensional Scaling
  • [Optional Lab]
    • Dimensionality Reduction Demo
    • Kernel PCA Practice
    • Multidimensional Scaling

• Week 6

  • Matix Factorization
  • [Optional Lab]
    • TF-IDF supplemental Demo
    • Non Negative Matrix Factorization Demo
    • Non Negative Matrix Factorization Practice

• [Week 7]

  • [Project]

Course 5 : Deep Learning and Reinforcement Learning

• Week 1

  • Neural Network
  • Optimization Using Gradient Descent
  • [Optional Lab]
    • Neural Network with Sklearn Demo
    • Introduction to Neural Networks Demo
    • Gradient Descent Demo

• Week 2

  • Backpropagation and Activation Function
  • Keras Library
  • [Optional Lab]
    • Backpropagation Demo
    • Keras Demo
    • Regression with Keras Demo
    • Loading Images with Keras Demo

• Week 3

  • Optimizers and Data Shuffling
  • [Optional Lab]
    • Hyperparameter Optimization for Keras
    • Optimizers Demo

• Week 4

  • Convolutional Neural Networks
  • [Optional Lab]
    • Categorical Cross Entropy
    • Images Convolution Demo
    • Padding, Pooling and Stride Demo
    • Channels and Flattening Demo
    • Training the network Demo
    • Convolutional Neural Network Demo

• Week 5

  • Transfer Learning

• [Optional Lab]

  • Transfer Learning Demo
  • Types of Model APIs in Keras Demo
  • Transfer Learning Examples with Existing Architectures
  • Regularization Techniques

• Week 6

  • Recurrent Neural Network and LSTM

• [Optional Lab]

  • Existing RNN Demo
  • Word Embeddings
  • Recurrent NN Demo
  • LSTM and GRU Demo
  • Types of Sequential Data

• week 7

  • Autoencoders
  • [Optional Lab]
    • Autoencoders Demo
    • Autoencoders Practice

• Week 8

  • Generative Adversarial Networks
  • [Optional Lab]
    • variational Autoencoder Demo
    • GANS LAB1
    • GANS LAB2
    • GPU with Keras

• Week 9

  • Reinforcement Learning
  • [Optional Lab]
    • Reinforcement Learning Demo
    • [Project]

Course 6 : Machine Learning Capstone

• [Week 1]

  • Recommender Systems

• Week 2

  • EDA and Feature Engineering
  • [Optional Lab]
    • Extract Bags of Words Features Demo
    • Calculate course similarity using BoW

• Week 3

  • Content Based Recommender System
  • Clustering Based Recommender System

• Week 4

  • Collaborative Filtering Based Recommender system using K Nearest Neighbor
  • Collaborative Filtering Based Recommender system using non negative matirx Factorization

• [Week 5]

  • ML Model Deployment

• [Week 6]

  • [Project]