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Quick Facts

Medium Of InstructionsMode Of LearningMode Of DeliveryFrequency Of Classes
EnglishSelf Study, Virtual Classroom, Campus Based/Physical ClassroomVideo and Text BasedWeekdays, Weekends

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesIIM Kashipur

The Syllabus

Session 1: Introduction to Machine Learning and Deep Learning
  • Introduction to AI
  • Branches of AI
  • AI and Machine Learning
  • AI and Deep Learning
  • ML and DL Applications for Business
Session 2: Mathematical Foundations for Machine Learning and Deep Learning
  • Linear Algebra
  • Calculus
Session 3: Statistical Foundations
  • Concepts of Probability
  • Distributions

Session 4 to 6: Introduction to Python
  • Basics of Python Programming
  • Operators and Expressions
  • Decision Statements
  • Loop Control Statements
  • Functions & Python Packages
  • Working with Files
  • Object Oriented Concepts

Session 7 to 9: Descriptive Analytics
  • Descriptive Statistics
  • Data and Distributions
  • Visual Exploratory Analytics

Session 10 & 11: Foundations of Inferential Analytics
  • Inferential Statistics and Hypothesis Testing

Session 12 & 13: Automated Data Collection Using Python

Session 14 & 16: Business Context: Prediction Machine Learning Context: Linear Regression
  • Simple Linear Regression
  • Multiple Regression
  • Regression Diagnostics
  • Regularization Methods – LASSO, RIDGE, ELNET

Session 17 & 18: Business Context: Forecasting Machine Learning Context: Forecasting
  • Time Series Regression
Session 19 & 20: Business Context: Prediction Machine Learning Context: Regression
  • Modelling non-linear relationships

Session 21 & 23: Business Context: Prediction Machine Learning Context: Classification
  • Classification Basics
  • Logistic regression, N-Bayes, Decision Trees, KNN, Support Vector Machines
  • Confusion Matrix
  • Cost-Benefit Analysis

Session 24 & 25: Business Context : Prediction Machine Learning Context: Ensemble Methods
  • Ensemble Methods
  • Random Forests
  • Bagging
  • Boosting

Session 26 to 28: Business Context: Segmentation Machine Learning Context: Clustering
  • Clustering Basics
  • k-means, hierarchical and dbscan clustering
  • Clustering diagnostics

Session 29 & 30: Business Context: Market Basket Analysis & Recommendations Machine Learning Context: Recommender System
  • Concepts of Market Basket Analysis
  • Association rule mining
  • Introduction to Collaborative Filtering

Session 31: Deep Learning Introduction
  • Data Concept of Learning
  • Comparison Machine Learning
  • Data Representation
Session 32: Introduction to Tensors
  • Tensors as data containers
  • Basic Tensor Operations
  • Types of Tensors
  • Tensors for Practice

Session 33 & 34: Network Architecture
  • Optimizers, Loss Functions, Activation Functions
Session 33 & 34: Deep Learning for Regression
  • Dense Layer Architecture and Use-Case for Regression
Session 33 & 34: Deep Learning for Classification
  • Dense Layer Architecture and Use-Case for Classification

Session 35: Recurrent Neural Networks
  • Introduction to RNN
  • Comparison with Dense Layer Architecture
  • Application of RNNs for sequence data
  • Popular types of RNN (LSTM and BiLSTM)
Session 36: Recurrent Neural Networks
  • RNN for Uni-variate Data
  • RNN for Multivariate Data
  • RNN Optimization

Session 37: Convolutional Neural Networks
  • Introduction to CNN
  • Comparison with Dense Layer Architecture
  • Convnet architecture – Layers
  • Convnet architecture – Pre-processing
Session 38: Convolutional Neural Networks
  • Convnet architecture – Data Augmentation
  • Convnet architecture – Fine Tuning
  • CNNs using a pre-trained model
  • Visualizing convnet learning

Session 39: Business Context: Learning from Text Data Machine Learning Context: Text Analytics
  • Introduction to Text Analytics Process & Applications
  • NLTK, scikit-learn
  • Building & Managing Corpus
  • Data Wrangling and Text Pre-Processing
  • Text Vectorization'
    • a.BoW Model
    • b.One-hot encoding
    • c.Frequency Vector
    • d.TF-IDF
    • e.Word Embeddings
Session 40: Business Context: Text Analytics Application Machine Learning Context: Supervised Learning
  • Text Classification
  • Sentiment Analysis
Session 40: Machine Learning Context: Unsupervised Learning
  • Topic Modelling
  • Sentiment Analysis

Session 41 & 42: Web-services for Machine Learning

Session 43: Ethical Issues and Governance in AI

Session 44 & 45- Introduction to GAN
  • Architecture
  • Generator Network
  • Discriminator Model
  • Adversarial Network
  • Setting up and Training GAN

Session 46 & 47: Introduction to RL
  • Overview of Environments in RL
  • Formulation of problems in RL
  • Q-learning methods for RL
  • Applications using RL

Session 48 & 49: Student Project Presentations
Session 50 & 51: Student Project Presentations

Instructors

Articles

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