Course Summery

Introduction: 
It is a short-term course of six months. This course is for the 10+2 and higher students. Students of NIELIT 'O'/'A' Levels can also be admitted to this course. 

Eligibility:
(1) Higher Secondary

Part 1  (Python basics)

1. 1) Introduction to artificial intelligence
2. 2) Programming with Python
3. 3) Python - Operators, Expressions and Python Statements
4. 4) Conditional Statements and Loops
5. 5) Sequence Data Types – List, Tuple, set
6. 6) Input and Output in Python
7. 7) Dictionary, functions, Lambda Functions
8. 8) Modules and Functions in Python
9. 9) NumPy-arrays, indexing, slicing and iterating.

Part 2 (Data Science)

1. 1) Data Science Concepts
2. 2) Advanced concepts in Numpy
3. 3) Pandas – Data frame, Series using python
4. 4) Reading and Writing data from Excel/CSV formats into Pandas
5. 5) Merging, Concatenating, Group by and aggregation on data frames
6. 6) Statistical Concepts and Functions
7. 7) Time Series Analysis and its models
8. 8) Data visualization using Matplotlib
9. 9) Grids, axes, plots, colors, fonts and styling
10. 10) Types of plots - bar graphs, pie chart

Part 3 (Machine learning)

1. 1) Machine Learning - Categories of ML, Supervised, Unsupervised, Reinforcement, Semi Supervised.
2. 2) Regression, Classification, Naive Bayes, Support Vector Machines, Decision Trees, K-nearest Neighbors, Ensemble Methods of Classification, Machine Learning Evaluation Metrics, Overfitting and Under fitting, Cross Validation,
3. 3) Unsupervised - What is Clustering & its Use Cases, K-means Clustering, K- means algorithm, Hierarchical clustering, Hierarchical Clustering algorithm, High-dimensional clustering, Dimension Reduction-PCA
4. 4) Implementing different types of Supervised Learning algorithms

Part4 (Deep learning and Neural network)

1. 1) Artificial Neural Networks – ANN structure, Feed Forward Neural network, Back Propagation.
2. 2) Deep Learning Concepts, Convolutional Neural Network (CNN), Neural Network using Tensorflow.
3. 3) Learning Algorithms, Error correction and Gradient Descent Rules, Perceptron Learning Algorithm. Keras and PyTorch elements
4. 4) Computer Vision – Face Recognition and Detection with OpenCV, Face Recognizers, Training data, Prediction.