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MUST WATCH BEFORE YOU START
IF YOU ARE A NON CODER
IF YOU ARE FROM CODING BACKGROUND
IF YOU ARE FROM TEACHING FRATERNITY
AIGENXT
ROAD MAP TO AI
Prerequisite
READ THIS !!
Intro to AI and Its family
Elements in AI and Challenges faced
Categories of AI
Usage and Implementation
TEST YOUR KNOWLEDGE
Getting Started
Introduction to Google Colab
Steps for Google Colab setup
Frequently asked questions ( FAQ )
PYTHON
Hello World and Data Types
Math Operations and Booleans
Art of Printing
String Basics
String Slicing
Conditions
While Loops
Range and For Loops
List
Tuple
Dictionary and Set
Functions
Class - Methods, Attributes
Exception Handling
assessment
Modules and Libraries
Numpy - Basic
Numpy - Array Creation and Math Operations
Numpy - Array Concat and Reshaping
Random Library
OS Library
Time Library
assessment
Statistics and Probability
Introduction to Statistics and its types
Central Tendency, SD and Variance
Data Sources
Probability
Mutually Exclusive Events and Independent Events
Binomial Distribution Theory
Binomial Distribution Code
Poisson Distribution Theory
Poisson Distribution Code
Gaussian/Normal Distribution Theory
Gaussian/Normal Distribution Code
Assessment
EXPLORATORY DATA ANALYSIS
Basic Plotting
Loading External Data and Understanding
Visualization - Bi Variant Analysis
Visualization - Uni Variant Analysis
Visualization - Multi Variant Analysis
Data Cleaning - 1
Data Cleaning - 2
Assessment
LINEAR REGRESSION
Theory - Introduction and Measure of Association
Theory - Co-Variance and Correlation
Theory - Residual and MSE
P1-Hands On - Car Price prediction
Hands On - Fine tuning model
Assessment
CLASSIFICATION MODELS
Theory - Introduction to Classification
Theory - Decision Trees
P2 - Hands on - Decision Tree Implementation and Fine tuning
Accuracy Metrics - Precision, Recall, F1 Score
Decision Tree - Feature Splits
Theory - Random Forest
P3 - Hands on - Random Forest Implementation and Fine tuning
Theory - K Nearest Neighbors
P4 - Hands on - KNN Implementation and Fine tuning
Theory - Support Vector Machines
P5 - Hands on - SVM Implementation and Fine tuning
Theory - Logistic Regression
P6 - Hands on - Logistic Regression Implementation and Fine tuning
Theory - Gaussian Naïve Bayes
P7 - Hands on - GNB Implementation and Fine tuning
Assessment
IMAGE PROCESSING
Hello World To Image Processing
Drawing Operations
Basic Image Handling
Masking
Smoothing and Blurring
Thresholding
P8 - Contours and License Plate Detection
Assessment
IMAGE BASED MACHINE LEARNING
Feature Extraction and Challenges Faced
P9 - Haralick Textures
P10 - Multi - Feature Extraction - Sign Board Classification
P11 - Face Detection - Haar Cascade Classifier
P12 - Local Binary Pattern Histogram - Face Recognition
P13 - Histograms of Oriented Gradients
Assignment - Handwritten character Recognition
Assignment - Face Emotion Recognition System
Assignment - Numerical Data Classification
Assessment
UNSUPERVISED CLASSIFICATION
Unsupervised Classification
Theory - K Means and DB clustering
P14 - Hands on - KMeans Clustering
Hands on - DB Scan
Assignment - MNIST Clustering
Assessment
DEEP LEARNING
Introduction to Neural Networks
Neuron, Layers, Activation Functions
Forward and Back Propagation
Gradient Optimization and weight Updation
P15 - Hands on - Multi Layer Perceptron for Numerical data
Hands on - Model Fine tuning
P16 - Categorical Data and K Fold Validation
P17 - Hands on - MLP for Images
Hands on - Model Analysis and saving
Introduction to CNN
Convolution and Pooling Layers
P18 - CNN Implementation
P19 - Car Classification using CNN with Callbacks
Transfer learning
P20 - Transfer Learning implementation
Assignment - Classification with localization
Assignment - Plant Disease Classification
Assessment
Final Assessment
BONUS CONTENT
Introduction to Object detection
Introduction to Yolo Architecture
P21 - Implementing Yolo Architecture and model training
Model Inference
Introduction to GAN
Assignment - Custom Object Detection for helmet detection