14^th Dec 2026 to 15^th Jan 2027

           IIT Kanpur Certificate Program on PYTHON
for Artificial Intelligence Machine Learning and Deep Learning   

Below is the detailed day-to-day & slot-wise program with Lecture Modules & Hands-On Coding Sessions for the   IIT Kanpur Certification Program on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) from 14^th Dec 2026 to 15^th Jan 2027

	Week-1
	14th Dec , 2026
06:00 PM - 07:30 PM	Zoom Test Session
7:30 PM-8:00 PM	Break
08:00 PM - 9:15 PM	Lecture 1: Introduction to Artificial Intelligence (AI) Machine Learning (ML)  •   Overview of AI, ML  •   Regression  •   Classification  •   Supervised Learning  •   Unsupervised Learning  •   Deep Learning  •   Test-Train Split  •   Metrics
	15th Dec, 2026
06:00 PM - 7:30 PM	Lecture 2: Linear Algebra for AIML  •   Vector Representation  •   Inner Product  •   Orthogonality  •   Matrices  •   Matrix Inversion
7:30 PM-8:00 PM	Break
6:00 PM - 7:30 PM	Lecture 3: Linear Regression for AIML  •   Regression Model  •   Multiple Regressors  •   Model Computation  •   Pseudo inverse  •   Online Learning
	16th Dec, 2026
	Break Day
	17th Dec, 2026
6:00 PM - 7:30 PM	Project 1: IRIS Dataset Regression using PYTHON  •   IRIS Dataset Features  •   Linear Regression Module  •   Mean Squared Error (MSE)  •   R2 Score Project 2: Boston Housing Price Analysis using PYTHON-Based Regression  •   Boston Housing set Features  •   Model Fitting  •   Model Performance  •   MSE, R2 Score  •   Regression Plot
7:30 PM-8:00 PM	Break
06:00 PM - 07:30 PM	Lecture 4: Logistic Regression-Based AIML  •   Logistic Function  •   Class Probabilities  •   Likelihood and ML  •   Logistic Regression Metrics  •   Confusion Matrix
	18th Dec, 2026
06:00 PM - 07:30 PM	Project 3: SCIKIT Package for Logistic Regression using Purchase/ Shopping Data  •   Purchase/ Shopping Dataset Features  •   Logistic Model Fitting  •   Confusion Matrix Display  •   Accuracy Score
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 5: Support Vector Classifier (SVC) for Machine Learning  •   SVM Structure  •   Maximum Margin Classifier  •   Convexity and Convex Optimization  •   Kernel SVM
	Week-2
	21st Dec, 2026
06:00 PM - 7:30 PM	Project 4: IRIS Data Set classification using PYTHON-Based SVC  •   Dataset Features  •   Accuracy Metrics  •   Performance Evaluation
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 6: Naïve Bayes Technique for AIML  •   Feature Vector  •   Likelihood and Prior Probabilities  •   Naïve Bayes Principle  •   Posterior Probability Evaluation  •   Gaussian Naïve Bayes  •   Movie Recommender System Example
	22nd Dec, 2026
06:00 PM - 7:30 PM	Project 5: Breast Cancer Dataset Analysis using SVC  •   Breast Cancer Dataset Features  •   SVM Fit  •   Gaussian Kernel  •   Polynomial Kernel  •   Sigmoid Kernel  •   Performance Assessment Project 6: Naive Bayes Classification of Purchase Dataset  •   Purchase Dataset Features  •   Gaussian NB Model Fitting  •   Accuracy Metrics  •   Confusion Matrix Display
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 7: Discriminant Analysis (LDA) Based Data Classification  •   Gaussian Density  •   Multivariate Gaussian  •   Gaussian/ Linear Discriminant  •   Example Model Computation
	23rd Dec, 2026
06:00 PM - 7:30 PM	Project 7: Discriminant Based Data Classification using IRIS Data Set  •   IRIS Dataset Features  •   LDA Model Fitting  •   Performance Visualization
7:30 PM-8:00 PM	Break
08:00 PM - 9:15 PM	Lecture 8: Data Clustering for Machine Learning  •   K-Means Algorithm  •   Centroid Computation  •   Cluster Assignment  •   Elbow Method for Number of Clusters  •   Silhouette Score
	24th Dec, 2026
06:00 PM - 7:30 PM	Project 8: PYTHON Project for Data Clustering  •   K-Means Implementation  •   Elbow Curve  •   Silhouette Plot  •   Cluster Visualization
7:30 PM-8:00 PM	Break
8:00 PM - 09:15 PM	Lecture 9:Decision Tree Classifiers (DTC) for AIML  •   Optimal Feature Selection  •   Entropy  •   Conditional Entropy  •   Information Gain  •   Computation for Practical Restaurant Reservation Example
	25th Dec, 2026
	Gazetted Holiday
	Week-3
	28th Dec, 2026
06:00 PM - 7:30 PM	Project 9: Building a DTC for IRIS Dataset using PYTHON  •   IRIS Dataset Features  •   DTC Model Fitting  •   Accuracy Score  •   Confusion Matrix
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 10: Introduction to Neural Networks (NNs)  •   Neuron Structure and Properties  •   ANN Model  •   Activation Functions  •   One Hot Encoding  •   Categorical Crossentropy
	29th Dec, 2026
06:00 PM - 7:30 PM	Project 10: Decision Tree Classifier using for Purchase Logistic Data Set  •   Purchase Dataset Features  •   DTC Visualization  •   DTC Prediction  •   Performance Metrics
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Leture 11: Deep Learning and Deep Neural Nets  •   Multi-layer Neural Networks  •   DNN Models  •   Dense and Sequential Architectures  •   NN Notation  •   Multi-layer Neural Nets  •   Gradient Descent  •   Backpropagation  •   Dropout Layers
	30th Dec, 2026
06:00 PM - 7:30 PM	Project 11: PYTHON-based Neural Network using PYTHON for Boston Housing Dataset  •   Boston Dataset Features  •   Sequential NN Model  •   Model Fitting  •   Training Epochs  •   Accuracy Performance
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 12: Convolutional Neural Networks (CNNs)  •   CNN Architectures  •   Convolution  •   Dot Product,  •   Padding  •   Hierarchical Structure  •   Max/ Average Pooling
	31st Dec, 2026
	Holiday
	01st Jan, 2027
	Holiday
	Week-4
	04nd Jan, 2027
06:00 PM - 7:30 PM	Project 12: Neural Network for analysis of Mobile Prices Dataset  •   Dataset Features  •   One Hot Encoding  •   Data Scaling  •   NN Model  •   Crossentropy  •   Adam Optimizer  •   Plots of Loss and Accuracy
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture I: Dr. Dheeraj Nagaraj Research Scientist, Google
	05th Jan, 2027
06:00 PM - 7:30 PM	Project 13: Deep Learning for Fashion Classification using the MNIST Fashion Data Set  •   Fashion MNIST (Modified National Institute of Standards and Technology)      Dataset Description  •   MNIST Dataset Features and Classes  •   CNN Modeling  •   CNN Training  •   Sparse Categorical Crossentropy  •   Loss/ Accuracy Plotting Project 14: Deep Learning for Digit Classification using MNIST Digit DataSet  •   MNIST Handwritten Digit Dataset Features  •   CNN Architecture  •   Data Encoding  •   Softmax Activation  •   Confusion Matrix  •   Plots of Loss and Accuracy
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Break
	6th Jan, 2027
	Break Day
	7th Jan, 2027
06:00 PM - 7:30 PM	Project 15: Deep Learning using the CIFAR Dataset  •   CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes)  •   Dataset Visualization  •   CNN Architecture  •   Model Building and Compilation  •   Classification Accuracy  •   Confusion Matrix Metrics
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 13: RNNs and LSTMs  •   Recurrent neural network  •   Long-term dependencies  •   Long Short Term Memory networks  •   Forget gate layer  •   Input gate layer  •   Output gate layer
	8th Jan, 2027
06:00 PM - 7:30 PM	Test prep 1:Test preparation 1  •   Interview problem solving session  •   Solution discussion
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 14: Transformers  •   Large language models  •   Transformer encoder  •   Query vector  •   Key vector  •   Attention mechanism  •   Value vector  •   Transformer decoder
	Week-5
	11th Jan, 2027
6:00 PM - 7:30 PM	Project 16: IMDB Dataset and Deep Learning for Movie Rating Classification  •   Internet Movie Database (IMDb) Dataset Description  •   Vectorization  •   Binary Crossentropy  •   Model Training  •   Training and Validation Accuracy Plots  •   Training and Validation Loss Plots
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture II: Dr. Arjun Radhakrishna Principal Research Manager, Microsoft Redmond, Washington, United States
	12th Jan, 2027
	Break Day
	13th Jan, 2027
6:00 PM-7:30 PM	Break
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture III: Dr. Sandeep Ponnuru ML and Computer Vision Engineer Santa Clara, California, United States
	14th Jan, 2027
	Break Day
	15th Jan, 2027
6:00 PM - 7:30 PM	Lecture 17: Share price prediction using LSTM  •   LSTM Module  •   Share price dataset  •   LSTM Architecture  •   Model compilation  •   Model fit and share price prediction
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Test prep 2: Test preparation 2  •   Interview problem solving session  •   Solution discussion