Lecture 1: Introduction to Python
Lecture 2: Python Basics - Part I
Lecture 3: Python Basics - Part II
Lecture 4(a): Tutorial on Python Library: Numpy
Lecture 4(b): Tutorial on Python Library: matplotlib
Lecture 4(c): Linear Algebra with Numpy
Lecture 5: Regression Models
Lecture 6: Unsupervised Learning: Clustering and Dimensionality Reduction
Lecture 7: Supervised Learning: Classification
Lecture 8: Random Forest Classifier (Speaker - Mr. Angshuman Paul, Research Scholar, ISI Kolkata)
Lecture 9-10(a): Introduction to Deep Learning and CNN
Lecture 10(b): Introduction to Pytorch
Lecture 11(a): Pytorch: Fully Connected Neural Network for Image Classification
Lecture 11(b): Pytorch: Convolutional Neural Network for Image Classification
1) Assignment 1 (submission deadline: 08-10-2018)
2) Assignment 2 (submission deadline: 08-10-2018)
3) Assignment 3 (submission deadline: 08-10-2018)
4) Assignment 4 (submission deadline: 28-10-2018)
5) Assignment 5 (submission deadline: 04-11-2018)
6) Assignment 6 (submission deadline: 30-11-2018)
Jupyter notebooks (*.ipynb) containing the solutions of the assignments are to be submitted to the email id - cds.pgdba2018@gmail.com
The name of the jupyter notebook file should follow the following convention - <RollNo>_<Name>_<AssignmentNo>.ipynb
For example if your name is Akash, roll no is 100, and you are submitting the solution of assignment no. 2 then the jupyter notebook file name is 100_akash_2.ipynb.
Click here to find the list of projects. The list contains 10 projects. Choose one among ten projects and try to complete the project.
1) Mastering Python for Data Science by Samir Madhavan
2) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
3) Think Stats: Probability and Statistics for Programmers by Allen B. Downey
4) Linear Algebra: Step by Step by Kuldeep Singh
5) Basics of Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python by Jason Brownlee
6) An Introduction to Statistical Learning with Applications in R by G. James, D. Witten, T. Hastie and R. Tibshirani
7) Introduction to Linear Algebra by Gilbert Strang
8) Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe
9) Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics by Justin Solomon
10) Pattern Recognition by Sergios Theodoridis and Konstantinos Koutroumbas
11) Data Mining and Analysis Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr.
12) Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville