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- IIM Kashipur
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- Post Graduate Certificate Programme In Machine Learning and Deep Learning
Post Graduate Certificate Programme In Machine Learning and Deep Learning
Part time, Online
12 Months
₹ 205,000
Quick facts
particular | details | ||
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
+1 more
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays, Weekends
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Course and certificate fees
Fees information
certificate availability
certificate providing authority
The syllabus
Conceptual Foundations
Session 1: Introduction to Machine Learning and Deep Learning
Session 2: Mathematical Foundations for Machine Learning and Deep Learning
- Linear Algebra
- Calculus
Session 3: Statistical Foundations
- Concepts of Probability
- Distributions
Machine Learning Foundations
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
Visual Exploratory & Descriptive Analytics
Session 7 to 9: Descriptive Analytics
- Descriptive Statistics
- Data and Distributions
- Visual Exploratory Analytics
Inferential Analytics
Session 10 & 11: Foundations of Inferential Analytics
- Inferential Statistics and Hypothesis Testing
Automated Data Collection
Session 12 & 13: Automated Data Collection Using Python
Predictive Analytics Supervised Machine Learning - Regression
Session 14 & 16: Business Context: Prediction Machine Learning Context: Linear Regression
- Simple Linear Regression
- Multiple Regression
- Regression Diagnostics
- Regularization Methods – LASSO, RIDGE, ELNET
Time Series Forecasting
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
Supervised Machine Learning- Classification
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
Advanced Machine Learning Techniques
Session 24 & 25: Business Context : Prediction Machine Learning Context: Ensemble Methods
- Ensemble Methods
- Random Forests
- Bagging
- Boosting
Unsupervised Machine Learning
Session 26 to 28: Business Context: Segmentation Machine Learning Context: Clustering
- Clustering Basics
- k-means, hierarchical and dbscan clustering
- Clustering diagnostics
Recommenation Systems
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
Deep Learning - Foundations
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
Deep Learning - Architectutre & Applications
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
Deep Learning - Recurrent Neural Networks
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
Deep Learning - Convolutional Neural Networks - Computer Vision
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
Text Analytics Using Machine Learning & Deep 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
Web - Services For Machine Learning
Session 41 & 42: Web-services for Machine Learning
Ethical Issues and Governance in AI
Session 43: Ethical Issues and Governance in AI
Generative Adversarial Networks
Session 44 & 45- Introduction to GAN
- Architecture
- Generator Network
- Discriminator Model
- Adversarial Network
- Setting up and Training GAN
Reinforcement Learning
Session 46 & 47: Introduction to RL
- Overview of Environments in RL
- Formulation of problems in RL
- Q-learning methods for RL
- Applications using RL
Capstone Project
Session 48 & 49: Student Project Presentations
Session 50 & 51: Student Project Presentations
Instructors
Dr Venkataraghavan K
Instructor
Freelancer
Ph.D
Dr Mayank Sharma
Assistant Professor
IIM Kashipur
Other Bachelors, Ph.D
Dr Rajiv Kumar
Assistant professor
IIM Kashipur
B.E /B.Tech, M.E /M.Tech., Ph.D
Dr Abhradeep Maiti
Professor
Freelancer
Other Masters, Ph.D
Dr Harish Kumar
Instructor
IIM Kashipur
M.E /M.Tech., Ph.D
Mr Shaukat Ali.
Instructor
Freelancer
Ph.D
Dr Sabyasachi Patra
Associate Professor
IIM Kashipur
Other Masters, Ph.D