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    • Modern Artificial Intelligence Masterclass Build 6 Projects

    Modern Artificial Intelligence Masterclass: Build 6 Projects

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    Udemy

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    Mode

    Online

    Fees

    ₹ 599 4099

    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

    • Introduction and Welcome Message
    • Introduction, Key Tips and Best Practices
    • Course Outline and Key Learning Outcomes
    • Get the Materials

    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

    Dr Ryan Ahmed
    Professor
    Udemy

    Other Masters, Ph.D, MBA

    Mr Mitchell Bouchard

    Mr Mitchell Bouchard
    Filmmaker
    Freelancer

    Other Bachelors

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