Modern Artificial Intelligence Masterclass Build 6 Projects
What you’ll learn
Deploy Emotion AI-based model using Tensorflow 2.0 Serving and use the model to make inference.
Understand the concept of Explainable AI and uncover the blackbox nature of Artificial Neural Networks and visualize their hidden layers using GradCam technique
Develop Deep Learning model to automate and optimize the brain tumor detection processes at a hospital.
Build and train AI model to detect and localize brain tumors using ResNets and ResUnet networks (Healthcare applications).
Understand the theory and intuition behind Segmentation models and state of the art ResUnet networks.
Build, train, deploy AI models in business to predict customer default on credit card using AWS SageMaker XGBoost algorithm.
Optimize XGBoost model parameters using hyperparameters optimization search.
Apply AI in business applications by performing customer market segmentation to optimize marketing strategy.
Understand the underlying theory and mathematics behind DeepDream algorithm for Art generation.
Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0.
Develop ANNs models and train them in Google Colab while leveraging the power of GPUs and TPUs.
Basic knowledge of programming is recommended but not required.
# Course Update June 2021: Added a study on Explainable AI with Zero CodingArtificial Intelligence (AI) revolution is here!“Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43. 9%. Deep Learning, one of the segments analyzed and sized in this study, displays the potential to grow at over 42. 5%.” (Source: globenewswire).AI is the science that empowers computers to mimic human intelligence such as decision making, reasoning, text processing, and visual perception. AI is a broader general field that entails several sub-fields such as machine learning, robotics, and computer vision.For companies to become competitive and skyrocket their growth, they need to leverage AI power to improve processes, reduce cost and increase revenue. AI is broadly implemented in many sectors nowadays and has been transforming every industry from banking to healthcare, transportation and technology.The demand for AI talent has exponentially increased in recent years and it’s no longer limited to Silicon Valley! According to Forbes, AI Skills are among the most in-demand for 2020.The purpose of this course is to provide you with knowledge of key aspects of modern Artificial Intelligence applications in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. The course covers many new topics and applications such as Emotion AI, Explainable AI, Creative AI, and applications of AI in Healthcare, Business, and Finance.One key unique feature of this course is that we will be training and deploying models using Tensorflow 2.0 and AWS SageMaker. In addition, we will cover various elements of the AI/ML workflow covering model building, training, hyper-parameters tuning, and deployment. Furthermore, the course has been carefully designed to cover key aspects of AI such as Machine learning, deep learning, and computer vision. Here’s a summary of the projects that we will be covering:· Project #1 (Emotion AI): Emotion Classification and Key Facial Points Detection Using AI · Project #2 (AI in HealthCare): Brain Tumor Detection and Localization Using AI· Project #3 (AI in Business/Marketing): Mall Customer Segmentation Using Autoencoders and Unsupervised Machine Learning Algorithms · Project #4: (AI in Business/Finance): Credit Card Default Prediction Using AWS SageMaker’s XG-Boost Algorithm (AutoPilot)· Project #5 (Creative AI): Artwork Generation by AI· Project #6 (Explainable AI): Uncover the Blackbox nature of AI Who this course is for:The course is targeted towards AI practitioners, aspiring data scientists, Tech enthusiasts, and consultants wanting to gain a fundamental understanding of data science and solve real world problems. Here’s a list of who is this course for:· Seasoned consultants wanting to transform industries by leveraging AI.· AI Practitioners wanting to advance their careers and build their portfolio.· Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business.· Tech enthusiasts who are passionate about AI and want to gain real-world practical experience.Course Prerequisites:Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science fundamentals and directly apply these skills to solve real world challenging business problems.
Section 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
Section 2: Emotion AI
Lecture 5 Project Introduction and Welcome Message
Lecture 6 Task #1 – Understand the Problem Statement & Business Case
Lecture 7 Task #2 – Import Libraries and Datasets
Lecture 8 Task #3 – Perform Image Visualizations
Lecture 9 Task #4 – Perform Images Augmentation
Lecture 10 Task #5 – Perform Data Normalization and Scaling
Lecture 11 Task #6 – Understand Artificial Neural Networks (ANNs) Theory & Intuition
Lecture 12 Task #7 – Understand ANNs Training & Gradient Descent Algorithm
Lecture 13 Task #8 – Understand Convolutional Neural Networks and ResNets
Lecture 14 Task #9 – Build ResNet to Detect Key Facial Points
Lecture 15 Task #10 – Compile and Train Facial Key Points Detector Model
Lecture 16 Task #11 – Assess Trained ResNet Model Performance
Lecture 17 Task #12 – Import and Explore Facial Expressions (Emotions) Datasets
Lecture 18 Task #13 – Visualize Images for Facial Expression Detection
Lecture 19 Task #14 – Perform Image Augmentation
Lecture 20 Task #15 – Build & Train a Facial Expression Classifier Model
Lecture 21 Task #16 – Understand Classifiers Key Performance Indicators (KPIs)
Lecture 22 Task #17 – Assess Facial Expression Classifier Model
Lecture 23 Task #18 – Make Predictions from Both Models: 1. Key Facial Points & 2. Emotion
Lecture 24 Task #19 – Save Trained Model for Deployment
Lecture 25 Task #20 – Serve Trained Model in TensorFlow 2.0 Serving
Lecture 26 Task #21 – Deploy Both Models and Make Inference
Section 3: AI in Healthcare
Lecture 27 Project Introduction and Welcome Message
Lecture 28 Task #1 – Understand the Problem Statement and Business Case
Lecture 29 Task #2 – Import Libraries and Datasets
Lecture 30 Task #3 – Visualize and Explore Datasets
Lecture 31 Task #4 – Understand the Intuition behind ResNet and CNNs
Lecture 32 Task #5 – Understand Theory and Intuition Behind Transfer Learning
Lecture 33 Task #6 – Train a Classifier Model To Detect Brain Tumors
Lecture 34 Task #7 – Assess Trained Classifier Model Performance
Lecture 35 Task #8 – Understand ResUnet Segmentation Models Intuition
Lecture 36 Task #9 – Build a Segmentation Model to Localize Brain Tumors
Lecture 37 Task #10 – Train ResUnet Segmentation Model
Lecture 38 Task #11 – Assess Trained ResUNet Segmentation Model Performance
Section 4: AI in Business (Marketing)
Lecture 39 Project Introduction and Welcome Message
Lecture 40 Task #1 – Understand AI Applications in Marketing
Lecture 41 Task #2 – Import Libraries and Datasets
Lecture 42 Task #3 – Perform Exploratory Data Analysis (Part #1)
Lecture 43 Task #4 – Perform Exploratory Data Analysis (Part #2)
Lecture 44 Task #5 – Understand Theory and Intuition Behind K-Means Clustering Algorithm
Lecture 45 Task #6 – Apply Elbow Method to Find the Optimal Number of Clusters
Lecture 46 Task #7 – Apply K-Means Clustering Algorithm
Lecture 47 Task #8 – Understand Intuition Behind Principal Component Analysis (PCA)
Lecture 48 Task #9 – Understand the Theory and Intuition Behind Auto-encoders
Lecture 49 Task #10 – Apply Auto-encoders and Perform Clustering
Section 5: AI In Business (Finance) & AutoML
Lecture 50 Project Introduction and Welcome Message
Lecture 51 Notes on Amazon Web Services (AWS)
Lecture 52 Task #1 – Understand the Problem Statement & Business Case
Lecture 53 Task #2 – Import Libraries and Datasets
Lecture 54 Task #3 – Visualize and Explore Dataset
Lecture 55 Task #4 – Clean Up the Data
Lecture 56 Task #5 – Understand the Theory & Intuition Behind XG-Boost Algorithm
Lecture 57 Task #6 – Understand XG-Boost Algorithm Key Steps
Lecture 58 Task #7 – Train XG-Boost Algorithm Using Scikit-Learn
Lecture 59 Task #8 – Perform Grid Search and Hyper-parameters Optimization
Lecture 60 Task #9 – Understand XG-Boost in AWS SageMaker
Lecture 61 Task #10 – Train XG-Boost in AWS SageMaker
Lecture 62 Task #11 – Deploy Model and Make Inference
Lecture 63 Task #12 – Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)
Section 6: Creative AI
Lecture 64 Project Introduction and Welcome Message
Lecture 65 Task #1 – Understand the Problem Statement & Business Case
Lecture 66 Task #2 – Import Model with Pre-trained Weights
Lecture 67 Task #3 – Import and Merge Images
Lecture 68 Task #4 – Run the Pre-trained Model and Explore Activations
Lecture 69 Task #5 – Understand the Theory & Intuition Behind Deep Dream Algorithm
Lecture 70 Task #6 – Understand The Gradient Operations in TF 2.0
Lecture 71 Task #7 – Implement Deep Dream Algorithm Part #1
Lecture 72 Task #8 – Implement Deep Dream Algorithm Part #2
Lecture 73 Task #9 – Apply DeepDream Algorithm to Generate Images
Lecture 74 Task #10 – Generate DeepDream Video
Section 7: Explainable AI with Zero Coding
Lecture 75 Explainable AI Dataset Download & Link to DataRobot
Lecture 76 Project Overview on Food Recognition with AI
Lecture 77 DataRobot Demo 1 – Upload and Explore Dataset
Lecture 78 DataRobot Demo 2 – Train AI/ML Model
Lecture 79 DataRobot Demo 3 – Explainable AI
Section 8: Crash Course on AWS, S3, and SageMaker
Lecture 80 What is AWS and Cloud Computing?
Lecture 81 Key Machine Learning Components and AWS Tour
Lecture 82 Regions and Availability Zones
Lecture 83 Amazon S3
Lecture 84 EC2 and Identity and Access Management (IAM)
Lecture 85 AWS Free Tier Account Setup and Overview
Lecture 86 AWS SageMaker Overview
Lecture 87 AWS SageMaker Walk-through
Lecture 88 AWS SageMaker Studio Overview
Lecture 89 AWS SageMaker Studio Walk-through
Lecture 90 AWS SageMaker Model Deployment
Seasoned consultants wanting to transform industries by leveraging AI.,AI Practitioners wanting to advance their careers and build their portfolio.,Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business.,Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business.
Udemy | English | 15h 46m | 12.72 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA