About

Trainer Information

Deepan is an expert in Python, AI and Image processing. He is a part of Indian book of records for conducting AI workshop in a single day for 700 students. Published White papers in Machine Learning and Artificial Intelligence. He has won many International and National Awards. He was personally appreciated by the former president of India, Dr. APJ Abdul Kalam for creating a cost effective Humanoid Robot.

The Training Program

Currently AI is dominating all the industries. So this syllabus will enable participants to become an AI expert starting from scratch. After this Course, participants can create AI applications on their own and can understand how to approach a problem using AI. They will become a pioneer in having a deep knowledge on implementing AI (rather becoming an research expert) The Industry needs people who can deploy existing algorithms in their application chain to make some smart automation.

Syllabus Information

1. Introduction
   1. Intro to AI and Its family
   2. Elements in AI and Challenges faced
   3. Categories of AI
   4. Usage and Implementation
2. Google Colab and Github
   1. Using Google Colab
   2. Github Colab Interaction
   3. How to use GitHub
3. Python Basics
   1. Beautifulsoup
   2. Hello World
   3. Data types and Math Operation
   4. Conditions
   5. Loops
   6. String Operation
   7. Data Structures
   8. Exception and Function
   9. Other basic info
4. Numpy
   1. Creating Basic Arrays
   2. Adding, removing, slicing & sort
   3. Array Math &Concat
   4. Reshape & Expand Dimensions
   5. Loading csv files and accessing it
5. Pandas and Plotting Libraries
   1. Pyinstaller
   2. Loading csv files and accessing it
   3. Basic Plotting using Matplotlib and Seaborn
   4. Multi Axis Plotting using Seaborn
6. Other Python Libraries
   1. OS
   2. Time
   3. Imutils
7. Image Processing
   1. Hello world to Image Processing
   2. Drawing Operations
   3. Basic Image Handling
   4. Masking Operation
   5. Removing Noise in images
   6. Thresholding
   7. Contours
8. Machine Learning
   1. Binary Class ‐ PIMA Dataset
   2. Acquiring and Vectoring Data
   3. Decision Trees & SK Learn Metrics
   4. Random Forest
   5. K-nearest Neighbours
   6. Support Vector Machine
   7. Logistic Regression
   8. Working with Multi Class Problem
9. Feature Extraction Techniques for Images
   1. Color Channel Statistics
   2. Haralick Texture
   3. Local Binary Pattern Histogram
   4. Histogram of Oriented Gradients
   5. SIFT
   6. Haar Cascade for Face Detection
10. Multi-Layer Perceptron (aka) Deep Neural Network
    1. MLP for Regression
    2. MLP for Binary Classification in CSV data
    3. MLP for Multi Classification in CSV data
    4. MLP for Image
    5. MLP for Sound
11. Convolution Neural Network
    1. Introduction to CNN
    2. Convolution and Pooling Layers
    3. Summing up CNN
    4. Creating a CNN Classifier
    5. Creating a CNN classifier with transfer Learning
12. Natural Language Processing
    1. Introduction to Natural Language Processing
    2. Feature Extraction for NLP
    3. Creating a sentimental analysis Neural Network for NLP
13. Recurrent Neural Network
    1. Introduction to RNN
    2. Introduction to LSTM and GRU
    3. Creating sentimental analysis Neural Network with LSTM for NLP
    4. Attention Network based Yes or No Chat bot
    5. Text generation using LSTM
14. Bonus
    1. Face Recognition
    2. Face mask Recognition
    3. Degradable/Non degradable Garbage Detection

Course Pre-Requisite

Joining the course has the below pre-requisites:-

1. Access to a Personal Computer or Laptop
2. 4 GB RAM and 256 GB Hard Disk Space
3. Python software installed (preferably Internet access)
4. Github and Google Account

Begin Enrollment 6369 learners have showed interest for this course