Machine Learning & Deep Learning

Duration: 456 hours + 11 days Project

Eligibility: Pass in CP SPF with minimum score 60% / Pass in FTP Java with minimum score 80% + Pass in 12 th Standard or equivalent with 70% marks in Mathematics

Program Structure

Module 1: Fundamentals of Linear Algebra (72hrs)

  • Systems of Linear Equations
  • Row Reduction and Echelon Forms
  • Matrix Operations, including inverses
  • Block Matrices
  • Linear Dependence and Independence
  • Subspaces, Bases and Dimensions
  • Orthogonal Bases and Orthogonal Projections
  • Gram-Schmidt Process
  • Linear Models and Least-Square Problems
  • Determinants and their properties
  • Cramer’s Rule
  • Eigenvalues and Eigenvectors
  • Diagonalization of a Matrix
  • Symmetric Matrices
  • Linear Transformations
  • Singular Value Decomposition
  • Module Assessment Test

Module 2: Statistics and Probability (72hrs)

  • Counting
  • Random Variables, Distributions, Quantities, and Mean Variance
  • Conditional Probability, Bayes’ theorem, and Base Rate Fallacy
  • Joint Distributions, Covariance, Correlation, and Independence
  • Central Limit Theorem
  • Bayesian Inference with Known Priors, and Probability Intervals
  • Conjugate Priors
  • Bayesian Inference with Unknown Priors
  • Frequentist Significance Tests, and Confidence Intervals
  • Resampling Methods: Bootstrapping
  • Linear Regression
  • Module Assessment Test

Module 3: Linux (72hrs)

  • Linux Foundation
  • Linux Filesystem Tree Layout
  • Processes
  • Signals
  • Package Management Systems
  • RPM (Red Hat Package Manager)
  • dpkg (package manager for Debian-based systems)
  • YUM (Yellowdog Updater Modified)
  • Zypper
  • APT (Advanced Package Tool)
  • System Monitoring
  • Process Monitoring
  • Memory Management and Usage
  • I/O Monitoring and Tuning
  • I/O Scheduling
  • Disk Partitioning
  • Version Control
  • Introduction and Working on Git on Linux
  • Module Assessment Test

Module 4: Python Programming Language with OOPs in Python (96 hours + 4 days Project)

  • Why Code in Python
  • Different Parts of the Python Program
  • The Python Interpreter
  • Data Types
  • Files and everything else
  • Python Statement and Syntax
  • Iterations and Comprehensions
  • Functions
  • Modules
  • Classes & OOPs
  • Exception and Tools
  • Project: Web Development Project using Python & HTML, JS & CSS
  • Module Assessment Test

Module 5: Machine Learning & Deep Learning (144hrs + 7 days Project)

  • Numpy
  • Pandas
  • Scikit-learn
  • Tensor Flow
  • Keras
  • Linear Aggression
  • Logistics Aggression
  • Trees and Ensemble Method
  • Neural Networks
  • ANN (Artificial Neural networks)
  • CNN (Convolutional Neural Network)
  • RNN (Recurrent Neural Network)
  • Support Vector Machines
  • Assessment Test
  • Final Project on Machine Learning up for submission