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Curriculum

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
  • Experiential learning project
  • Python Overview
  • Python Basics
  • Python functions, packages and routines
  • Pandas, NumPy, Matplotlib introduction
  • Pandas for Pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Seaborn for Data Visualization
  • Sci-kit Library
  • Case Studies and careers
  • Experiential Learning project
  • Introduction to Anaconda/Jupyter for coding/data visualisation
  • Introduction to Data Science, ML, AI
  • Supervised Learning
    • Introduction to Machine Learning
    • Supervised Learning concepts
    • Linear Regression (both Univariate and Multivariate)
    • Polynomial Regression (both Univariate and Multivariate)
    • Logistic Regression (Binary Class)
    • Logistic Regression (Multi Class)
    • K-NN Classification
    • Naive Bayesian classifiers
    • SVM - Support Vector Machines
    • Experiential Learning project
  • Unsupervised Learning
    • Unsupervised Learning concepts
    • Clustering approaches
    • K Means clustering
    • Hierarchical clustering
    • High-dimensional clustering
    • Expectation Maximization
  • Decision Trees
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Stacking
  • Experiential Learning project
  • PCA (Principal Component Analysis) and Its Applications
  • Confusion Matrix
  • Value based methods Q-learning
  • Policy based methods
  • User & item based recommendation systems
  • Collaborative filtering
  • Content based filtering
  • Hybrid recommendation systems
  • Performance measurement
  • Experiential Learning project
  • Text Analytics
  • Feature extraction
  • Model Defects & Evaluation Metrics
  • Model selection and tuning
  • Comparison of Machine Learning models
  • Experiential Learning project
  • Python (Pandas, Numpy, Scipy,
  • Matplotlib, Seaborn and Scikit-Learn)
  • Mini Projects
  • Machine Learning Lab session
  • Deep Learning
    • Neural Network Basics
    • Artificial Neural Network (ANN)
    • Forward Propagation
    • Backward Propagation
    • Deep Neural Networks
    • Recurrent Neural Networks (RNN)
    • Deep Learning applied to images using CNN
    • Tensor Flow for Neural Networks & Deep Learning
  • Convolutional Neural Networks
  • Keras library for deep learning in Python
  • Pre-processing image Data
  • Object & face recognition
  • Visualizing features & kernels
  • TensorBoard – Visualizing Learning, Graph Visualization
  • Synthesis and style transfer
  • Case Study: Visualizing a convoluted neural network
  • NLP library NLTK
  • Statistical NLP and text similarity
  • Syntax and parsing techniques
  • Text summarization techniques
  • Semantics and Generation
  • Uninformed and heuristic-based search techniques
  • Adversarial search and its uses
  • Planning and constraint satisfaction techniques
  • Python
  • Data libraries like Pandas, Numpy, Scipy
  • Python ML library scikit-learn
  • Python visualization library Matplotlib
  • NLP library NLTK
  • Tensor Flow
  • Keras
  • Group Presentation
  • Sl. No.
  • Chapter
  • Chapter - 1
  • Python for AI & Machine Learning
  • Chapter - 2
  • Understanding AI Through Data
  • Chapter - 3
  • Statistical Learning
  • Chapter - 4
  • Machine Learning:Supervised Learning
  • Chapter - 5
  • Machine Learning: UnSupervised Learning)
  • Chapter - 6
  • Ensemble techniques:Bagging,Boosting
  • Chapter - 7
  • Text Mining and Sentimental Analysis
  • Chapter - 8
  • Recommendation Systems
  • Chapter - 9
  • Neural Network
  • Chapter - 10
  • Natural Language Processing
  • Chapter - 11
  • Reinforcement Learning
  • Chapter - 12
  • Computer Vision(Open CV)
  • Chapter - 13
  • Capstone project

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Masters in Artificial Intelligence and Machine Learning , this Course and batch also available in other locations View

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