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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based | Weekdays, Weekends |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM 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
10 Jul'25, 09:51 AM
3 Apr'25, 06:57 AM
5 Nov'24, 12:00 PM
22 Apr'24, 10:28 AM
26 Mar'24, 07:31 AM
14 Feb'24, 11:49 AM
1 Feb'24, 10:53 AM
31 Jan'24, 09:37 AM
27 Dec'23, 11:21 AM
27 Sep'23, 06:07 PM
10 Jul'25, 09:51 AM
3 Apr'25, 06:57 AM
5 Nov'24, 12:00 PM
22 Apr'24, 10:28 AM
26 Mar'24, 07:31 AM
14 Feb'24, 11:49 AM
1 Feb'24, 10:53 AM
31 Jan'24, 09:37 AM
27 Dec'23, 11:21 AM
27 Sep'23, 06:07 PM