AIGENXT - Professional AI Program

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Course curriculum

1. IF YOU ARE A NON CODER
2. IF YOU ARE FROM CODING BACKGROUND
3. IF YOU ARE FROM TEACHING FRATERNITY
1. Level 1 & 2 Whatsapp Link
2. ROAD MAP TO AI
1. READ THIS !!
2. Intro to AI and Its family
3. Elements in AI and Challenges faced
4. Categories of AI
5. Usage and Implementation
6. TEST YOUR KNOWLEDGE
1. Introduction to Google Colab
2. Steps for Google Colab setup
3. Frequently asked questions ( FAQ )
1. Hello World and Data Types
2. Math Operations and Booleans
3. Art of Printing
4. String Basics
5. String Slicing
6. Conditions
7. While Loops
8. Range and For Loops
9. List
10. Tuple
11. Dictionary and Set
12. Functions
13. Class - Methods, Attributes
14. Exception Handling
15. Assessment
1. LEVEL 3 Whatsapp Link
2. Numpy - Basic
3. Numpy - Array Creation and Math Operations
4. Numpy - Array Concat and Reshaping
5. Random Library
6. OS Library
7. Time Library
8. Assessment 2
1. Introduction to Statistics and its types
2. Central Tendency, SD and Variance
3. Data Sources
4. Probability
5. Mutually Exclusive Events and Independent Events
6. Binomial Distribution Theory
7. Binomial Distribution Code
8. Poisson Distribution Theory
9. Poisson Distribution Code
10. Gaussian/Normal Distribution Theory
11. Gaussian/Normal Distribution Code
12. Assessment 3
1. LEVEL 4 Whatsapp Link
2. Basic Plotting
3. Loading External Data and Understanding
4. Visualization - Bi Variant Analysis
5. Visualization - Uni Variant Analysis
6. Visualization - Multi Variant Analysis
7. Data Cleaning - 1
8. Data Cleaning - 2
9. Assessment 4
1. LEVEL 5 Whatsapp Link
2. Theory - Introduction and Measure of Association
3. Theory - Co-Variance and Correlation
4. Theory - Residual and MSE
5. P1-Hands On - Car Price prediction
6. Hands On - Fine tuning model
7. Assessment 5
1. LEVEL 6 Whatsapp Link
2. Theory - Introduction to Classification
3. Theory - Decision Trees
4. P2 - Hands on - Decision Tree Implementation and Fine tuning
5. Accuracy Metrics - Precision, Recall, F1 Score
6. Decision Tree - Feature Splits
7. Theory - Random Forest
8. P3 - Hands on - Random Forest Implementation and Fine tuning
9. Theory - K Nearest Neighbors
10. P4 - Hands on - KNN Implementation and Fine tuning
11. Theory - Support Vector Machines
12. P5 - Hands on - SVM Implementation and Fine tuning
13. Theory - Logistic Regression
14. P6 - Hands on - Logistic Regression Implementation and Fine tuning
15. Theory - Gaussian Naïve Bayes
16. P7 - Hands on - GNB Implementation and Fine tuning
17. Assessment 6
1. LEVEL 7 Whatsapp Link
2. Hello World
3. Drawing Operations
4. Basic Image Handling
5. Masking
6. Smoothing and Blurring
7. Thresholding
8. P8 - Contours and License Plate Detection
9. Assessment 7
1. LEVEL 8 Whatsapp Link
2. Feature Extraction and Challenges Faced
3. P9 - Haralick Textures
4. P10 - Multi - Feature Extraction - Sign Board Classification
5. P11 - Face Detection - Haar Cascade Classifier
6. P12 - Local Binary Pattern Histogram - Face Recognition
7. P13 - Histograms of Oriented Gradients
8. Assignment - Handwritten character Recognition
9. Assignment - Face Emotion Recognition System
10. Assignment - Numerical Data Classification
11. Assessment 8
1. LEVEL 9 Whatsapp Link
2. Theory - Introduction to Clustering
3. Theory - K Means and DB clustering
4. P14 - Hands on - Kmeans Clustering
5. Hands on - DB Scan
6. Assignment - MNIST Clustering
7. Assessment 9
1. Introduction to Neural Networks
2. Neuron, Layers, Activation Functions
3. Forward and Back Propagation
4. Gradient Optimization and weight updation
5. P15 - Hands on - Multi Layer Perceptron for numerical data
6. Hands on - Model Fine tuning
7. P16 - Categorical Data and K Fold Validation
8. P17 - Hands on - MLP for Images
9. Hands on - Model Analysis and saving
10. Introduction to CNN
11. Convolution and Pooling Layers
12. P18 - CNN_Implementation
13. P19 - Car Classification using CNN with Callbacks
14. Transfer learning
15. P20 - Transfer Learning implementation
16. Assignment - Classification with localization
17. Assignment - Plant Disease Classification
18. Assessment 10
1. LEVEL 10 Whatsapp Link
2. Introduction to Object detection
3. Introduction to Yolo Architecture
4. P21 - Implementing Yolo Architecture and model training
5. Model Inference
6. Introduction to GAN
7. Assignment - Custom Object Detection for helmet detection
1. FINAL ASSESSMENT

About this course

₹9,999.00
135 lessons
17 hours of video content

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Course curriculum

MUST WATCH BEFORE YOU START

LEVEL 1 : AIGENXT

Prerequisite

LEVEL 2 : Getting Started With Google Colab

Python Basics

LEVEL 3 : Modules and Libraries

Statistics and Probability

LEVEL 4 : Exploratory Data Analysis

LEVEL 5 : Linear Regression

LEVEL 6 : Classification Models

LEVEL 7 : Image Processing

LEVEL 8 : Image based Machine Learning

LEVEL 9 : Unsupervised Classification

Deep Learning

LEVEL 10 : Bonus Content

THE FINAL ASSESSMENT

About this course

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