Artificial Intelligence and Machine Learning


Course Title: Artificial Intelligence and Machine Learning

Course Duration: 100 Hours


Course Outline


Module 1: Python Programming (6 Projects)


Basics of Programming

Variables, Strings and Numbers, Math Operators, Built-in functions, List and its methods, Tuples, Dictionaries, User Inputs, Conditionals, Custom Functions, Loops.


Numpy Arrays, Indexing, Slicing, Iterating, Stacking and Splitting, Numpy Operations.


Introduction, Series, Data Frames, Merging, Joining and Concatenating, Operations, Data i/p and o/p.


Data Visualisation, Bar Plot, Pair Plot, Histogram and Box-plot, Distribution Plots, Grids and Matrices.



Module 2: Statistics (3 Projects)


Probability, Combinations, Bayesian Inference, Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Advanced Statistical Methods.



Module 3: Machine Learning (10 Projects)


Supervised Learning


Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Ridge and Lasso Regression, Model and Performance Evaluation.


Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), Kernel SVM, Naïve Bayes, Decision Tree, Random Forest , Model and Performance Evaluation.

Unsupervised Learning

Clustering, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis

Ensemble Techniques

Max Voting, Averaging, Weighted Average, Stacking, Blending, Bagging & Boosting, AdaBoost, Gradient Boosting, XGBoost, CatBoost and Light GBM.



Module 4: Neural Networks and Deep Learning (30 Projects)


Neural Networks

Deep Learning Introduction, Shallow Neural Networks, Artificial Neural Networks, Deep Neural Networks, 3 Projects.

Digital Image Processing

Manipulate the images, Image Segmentation, Object Tracking, Object Detection, Feature Detection, Image Filtering, Transformations, Photo Denoising, Facial Landmarks, 10 Mini Projects.

Computer Vision

Convolutional Neural Networks , Regularization Methods, 2 Projects, Architectural design, Instance Segmentation, Semantic Segmentation, Object Detection, Video Analysis, SSD, Yolo, Mask-RCNN, 5 Real Time Projects.

Natural Language Processing

NLP Basics, Processing and understanding text, Feature Engineering, Text Classification, Text Summarization, Text Clustering, Semantic Analysis, Sentiment Analysis, Word Embedding, Recurrent Neural , Networks, FastText, Text-CNN, Text-LSTM, Bi-LSTM, 12 Real Time Projects.

Recommender Systems(RS)

Popularity Based RS, Content-Based Filtering, Collaborative Filtering, Hybrid Approaches.

Deployment Strategies

Model Pipelines and Applications, Model Architecture, API Development and CI/CD concepts, Paas and AWS deployment. 


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