- Introduction and Welcome Message
- Introduction, Key Tips and Best Practices
- Course Outline and Key Learning Outcomes
- Get the Materials
Modern Artificial Intelligence Masterclass: Build 6 Projects
Quick Facts
particular | details | |||
---|---|---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
Fees information
₹ 599 ₹4,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
Emotion AI
- Project Introduction and Welcome Message
- Task #1 - Understand the Problem Statement & Business Case
- Task #2 - Import Libraries and Datasets
- Task #3 - Perform Image Visualizations
- Task #4 - Perform Images Augmentation
- Task #5 - Perform Data Normalization and Scaling
- Task #6 - Understand Artificial Neural Networks (ANNs) Theory & Intuition
- Task #7 - Understand ANNs Training & Gradient Descent Algorithm
- Task #8 - Understand Convolutional Neural Networks and ResNets
- Task #9 - Build ResNet to Detect Key Facial Points
- Task #10 - Compile and Train Key Facial Points Detector Model
- Task #11 - Assess Trained ResNet Model Performance
- Task #12 - Import and Explore Facial Expressions (Emotions) Datasets
- Task #13 - Visualize Images for Facial Expression Detection
- Task #14 - Perform Image Augmentation
- Task #15 - Build & Train a Facial Expression Classifier Model
- Task #16 - Understand Classifiers Key Performance Indicators (KPIs)
- Task #17 - Assess Facial Expression Classifier Model
- Task #18 - Make Predictions from Both Models: 1. Key Facial Points & 2. Emotion
- Task #19 - Save Trained Model for Deployment
- Task #20 - Serve Trained Model in TensorFlow 2.0 Serving
- Task #21 - Deploy Both Models and Make Inference
AI in Healthcare
- Project Introduction and Welcome Message
- Task #1 - Understand the Problem Statement and Business Case
- Task #2 - Import Libraries and Datasets
- Task #3 - Visualize and Explore Datasets
- Task #4 - Understand the Intuition behind ResNet and CNNs
- Task #5 - Understand Theory and Intuition Behind Transfer Learning
- Task #6 - Train a Classifier Model To Detect Brain Tumors
- Task #7 - Assess Trained Classifier Model Performance
- Task #8 - Understand ResUnet Segmentation Models Intuition
- Task #9 - Build a Segmentation Model to Localize Brain Tumors
- Task #10 - Train ResUnet Segmentation Model
- Task #11 - Assess Trained ResUNet Segmentation Model Performance
AI in Business (Marketing)
- Project Introduction and Welcome Message
- Task #1 - Understand AI Applications in Marketing
- Task #2 - Import Libraries and Datasets
- Task #3 - Perform Exploratory Data Analysis (Part #1)
- Task #4 - Perform Exploratory Data Analysis (Part #2)
- Task #5 - Understand Theory and Intuition Behind K-Means Clustering Algorithm
- Apply Elbow Method to Find the Optimal Number of Clusters
- Task #7 - Apply K-Means Clustering Algorithm
- Task #8 - Understand Intuition Behind Principal Component Analysis (PCA)
- Task #9 - Understand the Theory and Intuition Behind Auto-encoders
- Task #10 - Apply Auto-encoders and Perform Clustering
AI In Business (Finance) & AutoML
- Project Introduction and Welcome Message
- Notes on Amazon Web Services (AWS)
- Task #1 - Understand the Problem Statement & Business Case
- Task #2 - Import Libraries and Datasets
- Task #3 - Visualize and Explore Dataset
- Task #4 - Clean Up the Data
- Task #5 - Understand the Theory & Intuition Behind XG-Boost Algorithm
- Task #6 - Understand XG-Boost Algorithm Key Steps
- Task #7 - Train XG-Boost Algorithm Using Scikit-Learn
- Task #8 - Perform Grid Search and Hyper-parameters Optimization
- Task #9 - Understand XG-Boost in AWS SageMaker
- Task #10 - Train XG-Boost in AWS SageMaker
- Task #11 - Deploy Model and Make Inference
- Task #12 - Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)
Creative AI
- Project Introduction and Welcome Message
- Task #1 - Understand the Problem Statement & Business Case
- Task #2 - Import Model with Pre-trained Weights
- Task #3 - Import and Merge Images
- Task #4 - Run the Pre-trained Model and Explore Activations
- Task #5 - Understand the Theory & Intuition Behind Deep Dream Algorithm
- Task #6 - Understand The Gradient Operations in TF 2.0
- Task #7 - Implement Deep Dream Algorithm Part #1
- Task #8 - Implement Deep Dream Algorithm Part #2
- Task #9 - Apply DeepDream Algorithm to Generate Images
- Task #10 - Generate DeepDream Video
Explainable AI with Zero Coding
- Explainable AI Dataset Download & Link to DataRobot
- Project Overview on Food Recognition with AI
- DataRobot Demo 1 - Upload and Explore Dataset
- DataRobot Demo 2 - Train AI/ML Model
- DataRobot Demo 3 - Explainable AI
Crash Course on AWS, S3, and SageMaker
- What is AWS and Cloud Computing?
- Key Machine Learning Components and AWS Tour
- Regions and Availability Zones
- Amazon S3
- EC2 and Identity and Access Management (IAM)
- AWS Free Tier Account Setup and Overview
- AWS SageMaker Overview
- AWS SageMaker Walk-through
- AWS SageMaker Studio Overview
- AWS SageMaker Studio Walk-through
- AWS SageMaker Model Deployment
Congratulations!! Don't forget your prize :)
- Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
Articles
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