Modern Artificial Intelligence Masterclass: Build 6 Projects

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction and Welcome Message

Lecture 2: Introduction, Key Tips and Best Practices

Lecture 3: Course Outline and Key Learning Outcomes

Lecture 4: Get the Materials

Chapter 2: Emotion AI

Lecture 1: Project Introduction and Welcome Message

Lecture 2: Task #1 – Understand the Problem Statement & Business Case

Lecture 3: Task #2 – Import Libraries and Datasets

Lecture 4: Task #3 – Perform Image Visualizations

Lecture 5: Task #4 – Perform Images Augmentation

Lecture 6: Task #5 – Perform Data Normalization and Scaling

Lecture 7: Task #6 – Understand Artificial Neural Networks (ANNs) Theory & Intuition

Lecture 8: Task #7 – Understand ANNs Training & Gradient Descent Algorithm

Lecture 9: Task #8 – Understand Convolutional Neural Networks and ResNets

Lecture 10: Task #9 – Build ResNet to Detect Key Facial Points

Lecture 11: Task #10 – Compile and Train Facial Key Points Detector Model

Lecture 12: Task #11 – Assess Trained ResNet Model Performance

Lecture 13: Task #12 – Import and Explore Facial Expressions (Emotions) Datasets

Lecture 14: Task #13 – Visualize Images for Facial Expression Detection

Lecture 15: Task #14 – Perform Image Augmentation

Lecture 16: Task #15 – Build & Train a Facial Expression Classifier Model

Lecture 17: Task #16 – Understand Classifiers Key Performance Indicators (KPIs)

Lecture 18: Task #17 – Assess Facial Expression Classifier Model

Lecture 19: Task #18 – Make Predictions from Both Models: 1. Key Facial Points & 2. Emotion

Lecture 20: Task #19 – Save Trained Model for Deployment

Lecture 21: Task #20 – Serve Trained Model in TensorFlow 2.0 Serving

Lecture 22: Task #21 – Deploy Both Models and Make Inference

Chapter 3: AI in Healthcare

Lecture 1: Project Introduction and Welcome Message

Lecture 2: Task #1 – Understand the Problem Statement and Business Case

Lecture 3: Task #2 – Import Libraries and Datasets

Lecture 4: Task #3 – Visualize and Explore Datasets

Lecture 5: Task #4 – Understand the Intuition behind ResNet and CNNs

Lecture 6: Task #5 – Understand Theory and Intuition Behind Transfer Learning

Lecture 7: Task #6 – Train a Classifier Model To Detect Brain Tumors

Lecture 8: Task #7 – Assess Trained Classifier Model Performance

Lecture 9: Task #8 – Understand ResUnet Segmentation Models Intuition

Lecture 10: Task #9 – Build a Segmentation Model to Localize Brain Tumors

Lecture 11: Task #10 – Train ResUnet Segmentation Model

Lecture 12: Task #11 – Assess Trained ResUNet Segmentation Model Performance

Chapter 4: AI in Business (Marketing)

Lecture 1: Project Introduction and Welcome Message

Lecture 2: Task #1 – Understand AI Applications in Marketing

Lecture 3: Task #2 – Import Libraries and Datasets

Lecture 4: Task #3 – Perform Exploratory Data Analysis (Part #1)

Lecture 5: Task #4 – Perform Exploratory Data Analysis (Part #2)

Lecture 6: Task #5 – Understand Theory and Intuition Behind K-Means Clustering Algorithm

Lecture 7: Task #6 – Apply Elbow Method to Find the Optimal Number of Clusters

Lecture 8: Task #7 – Apply K-Means Clustering Algorithm

Lecture 9: Task #8 – Understand Intuition Behind Principal Component Analysis (PCA)

Lecture 10: Task #9 – Understand the Theory and Intuition Behind Auto-encoders

Lecture 11: Task #10 – Apply Auto-encoders and Perform Clustering

Chapter 5: AI In Business (Finance) & AutoML

Lecture 1: Project Introduction and Welcome Message

Lecture 2: Notes on Amazon Web Services (AWS)

Lecture 3: Task #1 – Understand the Problem Statement & Business Case

Lecture 4: Task #2 – Import Libraries and Datasets

Lecture 5: Task #3 – Visualize and Explore Dataset

Lecture 6: Task #4 – Clean Up the Data

Lecture 7: Task #5 – Understand the Theory & Intuition Behind XG-Boost Algorithm

Lecture 8: Task #6 – Understand XG-Boost Algorithm Key Steps

Lecture 9: Task #7 – Train XG-Boost Algorithm Using Scikit-Learn

Lecture 10: Task #8 – Perform Grid Search and Hyper-parameters Optimization

Lecture 11: Task #9 – Understand XG-Boost in AWS SageMaker

Lecture 12: Task #10 – Train XG-Boost in AWS SageMaker

Lecture 13: Task #11 – Deploy Model and Make Inference

Lecture 14: Task #12 – Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)

Chapter 6: Creative AI

Lecture 1: Project Introduction and Welcome Message

Lecture 2: Task #1 – Understand the Problem Statement & Business Case

Lecture 3: Task #2 – Import Model with Pre-trained Weights

Lecture 4: Task #3 – Import and Merge Images

Lecture 5: Task #4 – Run the Pre-trained Model and Explore Activations

Lecture 6: Task #5 – Understand the Theory & Intuition Behind Deep Dream Algorithm

Lecture 7: Task #6 – Understand The Gradient Operations in TF 2.0

Lecture 8: Task #7 – Implement Deep Dream Algorithm Part #1

Lecture 9: Task #8 – Implement Deep Dream Algorithm Part #2

Lecture 10: Task #9 – Apply DeepDream Algorithm to Generate Images

Lecture 11: Task #10 – Generate DeepDream Video

Chapter 7: Explainable AI with Zero Coding

Lecture 1: Explainable AI Dataset Download & Link to DataRobot

Lecture 2: Project Overview on Food Recognition with AI

Lecture 3: DataRobot Demo 1 – Upload and Explore Dataset

Lecture 4: DataRobot Demo 2 – Train AI/ML Model

Lecture 5: DataRobot Demo 3 – Explainable AI

Chapter 8: Crash Course on AWS, S3, and SageMaker

Lecture 1: What is AWS and Cloud Computing?

Lecture 2: Key Machine Learning Components and AWS Tour

Lecture 3: Regions and Availability Zones

Lecture 4: Amazon S3

Lecture 5: EC2 and Identity and Access Management (IAM)

Lecture 6: AWS Free Tier Account Setup and Overview

Lecture 7: AWS SageMaker Overview

Lecture 8: AWS SageMaker Walk-through

Lecture 9: AWS SageMaker Studio Overview

Lecture 10: AWS SageMaker Studio Walk-through

Lecture 11: AWS SageMaker Model Deployment

Chapter 9: Congratulations!! Don't forget your Prize 🙂

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)