Tutors

Innomatics Research Labs

PROGRAM OVERVIEW

CURRICULUM

  Task - Streamlit

  • Project - Find the Nearest Pub

  Introduction to ML and Data Preprocessing - Text and Image Preprocessing

  • AI vs ML vs DL
  • Solving a ML Problem
  • Supervised Learning vs Unsupervised Learning
  • Quiz Discussion and Data Preprocessing
  • ML Framework and Data Preprocessing - Case Study
  • Case Study Discussion and Maths for ML
  • Mathematics for Machine Learning - Vectors, Matrices and Distances
  • Mathematics for Machine Learning - Dot Products and Distance Metrics
  • KNN for Classification
  • KNN for Regression and Quiz
  • Introduction to NLP
  • Text Preprocessing and CountVectorizer
  • NLP Case Study - Email Spam Classifier
  • Introduction to Image Preprocessing using PIL
  • Image Manipulation and Case Study - Recognising Handwritten Digits

  NLP - Embedding Techniques

  • Embedding Techniques - W2v
  • Embeddings - W2v, GloVe(Pretrained) and BERT(Pretrained)

  ML Algorithms

  • Probabilty Revisited
  • Introduction to Naive Bayes
  • Naive Bayes Derivation and Example (MAP)
  • Introduction to Decision Trees
  • Entropy and Information Gain for DT
  • Solving More Examples using DT
  • DT for Regression and Random Forest
  • Equation of a line, plane and hyperplane
  • Linear Regression Intuition
  • Linear Regression Derivation
  • Introduction to Gradient Descent
  • Gradient Descent for Linear Regression
  • Logistic Regression
  • Logistic Regression - Sigmoid Function
  • Logistic Regression - MLE
  • Assumptions of Linear Regression and Logistic Regression
  • Evaluation Metrics for Regression
  • Evaluation Metrics for Classification
  • Precision vs Recall and ROC AUC
  • Introduction to Model Selection
  • Model Selection - Overfitting vs Underfitting
  • Model Selection - Treating Overfitting and Underfitting Issue
  • Model Selection - Hyperparameters vs Parameters
  • Model Selection - Hyperparameter Tuning
  • Hyperparameter Tuning - Cross Validation
  • GridSearchCV and RandomizedSearchCV
  • Ensemble - Part 1
  • Ensemble - Part 2
  • Supervised Learning - Misc
  • Unsupervised Learning - Misc

  Misc Topics

  • Feature Engineering - 1
  • Feature Engineering - 2
  • Feature Engineering - Part 3
  • DT for Regression and SVM
  • SVM with Kernel Trick
  • SVM and Logistic Regression with 0-1 loss function
  • Assumption of Linear Regression and Treating Multicollinearity
  • Unsupervised Learning - K Means Clustering Algorithm
  • K Means++ and Hierarchical Clustering with Lab
  • Customer Segmentation and PCA
  • Principal Component Analysis with Lab

  Assignments/Project

  • Case Study - Banking Domain (Loan Status Prediction)
  • Case Stuy - Telecommunication Domain (Churn Prediction)
  • Text Data - Sentiment Analysis
  • Image Data - Handwritting Classification
  • Decision Tree for Regression
  • EDA - Credit Risk Scoring
  • EDA - Churn Prediction
  • Project - Predicting the Temperature

  Statistics

  • Probability
  • Random variable, Bernoulli and Binominal Distribution
  • Binominal and Uniform Distribution
  • Normal Distribution
  • Normal Distribution Cont.
  • Revision of Statistics
  • PDF of Normal DIst. and Z - Score
  • Pareto, Log Normal Distribution and QQ plot
  • Normal dist. and CRISP Framework
  • Sampling
  • Central Limit Theorem
  • Central Limit Theorem - II
  • Z - Score & Confidence Ievel
  • z - score and t - score
  • Z test & T test
  • Hypothesis Testing
  • Hypothesis Testing - II
  • Chi Square