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# Course Update June 2021: Added a study on Explainable AI with Zero Coding

Artificial 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.

Bạn sẽ học được gì

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.

Yêu cầu

  • Basic knowledge of programming is recommended but not required.

Nội dung khoá học

8 sections

Introduction

4 lectures
Introduction and Welcome Message
03:19
Introduction, Key Tips and Best Practices
10:42
Course Outline and Key Learning Outcomes
17:54
Get the Materials
00:05

Emotion AI

22 lectures
Project Introduction and Welcome Message
02:50
Task #1 - Understand the Problem Statement & Business Case
11:15
Task #2 - Import Libraries and Datasets
12:25
Task #3 - Perform Image Visualizations
09:35
Task #4 - Perform Images Augmentation
16:51
Task #5 - Perform Data Normalization and Scaling
07:44
Task #6 - Understand Artificial Neural Networks (ANNs) Theory & Intuition
20:32
Task #7 - Understand ANNs Training & Gradient Descent Algorithm
18:02
Task #8 - Understand Convolutional Neural Networks and ResNets
13:00
Task #9 - Build ResNet to Detect Key Facial Points
12:45
Task #10 - Compile and Train Facial Key Points Detector Model
07:40
Task #11 - Assess Trained ResNet Model Performance
04:54
Task #12 - Import and Explore Facial Expressions (Emotions) Datasets
12:00
Task #13 - Visualize Images for Facial Expression Detection
07:22
Task #14 - Perform Image Augmentation
13:31
Task #15 - Build & Train a Facial Expression Classifier Model
14:57
Task #16 - Understand Classifiers Key Performance Indicators (KPIs)
14:13
Task #17 - Assess Facial Expression Classifier Model
13:35
Task #18 - Make Predictions from Both Models: 1. Key Facial Points & 2. Emotion
07:37
Task #19 - Save Trained Model for Deployment
10:01
Task #20 - Serve Trained Model in TensorFlow 2.0 Serving
04:24
Task #21 - Deploy Both Models and Make Inference
08:23

AI in Healthcare

12 lectures
Project Introduction and Welcome Message
02:40
Task #1 - Understand the Problem Statement and Business Case
16:34
Task #2 - Import Libraries and Datasets
11:37
Task #3 - Visualize and Explore Datasets
20:44
Task #4 - Understand the Intuition behind ResNet and CNNs
10:37
Task #5 - Understand Theory and Intuition Behind Transfer Learning
11:50
Task #6 - Train a Classifier Model To Detect Brain Tumors
21:07
Task #7 - Assess Trained Classifier Model Performance
09:04
Task #8 - Understand ResUnet Segmentation Models Intuition
13:23
Task #9 - Build a Segmentation Model to Localize Brain Tumors
14:20
Task #10 - Train ResUnet Segmentation Model
04:05
Task #11 - Assess Trained ResUNet Segmentation Model Performance
12:27

AI in Business (Marketing)

11 lectures
Project Introduction and Welcome Message
02:10
Task #1 - Understand AI Applications in Marketing
07:16
Task #2 - Import Libraries and Datasets
13:50
Task #3 - Perform Exploratory Data Analysis (Part #1)
16:46
Task #4 - Perform Exploratory Data Analysis (Part #2)
19:17
Task #5 - Understand Theory and Intuition Behind K-Means Clustering Algorithm
16:57
Task #6 - Apply Elbow Method to Find the Optimal Number of Clusters
08:47
Task #7 - Apply K-Means Clustering Algorithm
15:54
Task #8 - Understand Intuition Behind Principal Component Analysis (PCA)
10:32
Task #9 - Understand the Theory and Intuition Behind Auto-encoders
08:39
Task #10 - Apply Auto-encoders and Perform Clustering
13:04

AI In Business (Finance) & AutoML

14 lectures
Project Introduction and Welcome Message
02:39
Notes on Amazon Web Services (AWS)
00:27
Task #1 - Understand the Problem Statement & Business Case
11:01
Task #2 - Import Libraries and Datasets
04:46
Task #3 - Visualize and Explore Dataset
20:43
Task #4 - Clean Up the Data
06:02
Task #5 - Understand the Theory & Intuition Behind XG-Boost Algorithm
20:45
Task #6 - Understand XG-Boost Algorithm Key Steps
19:48
Task #7 - Train XG-Boost Algorithm Using Scikit-Learn
07:53
Task #8 - Perform Grid Search and Hyper-parameters Optimization
06:57
Task #9 - Understand XG-Boost in AWS SageMaker
07:14
Task #10 - Train XG-Boost in AWS SageMaker
14:25
Task #11 - Deploy Model and Make Inference
09:42
Task #12 - Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)
13:10

Creative AI

11 lectures
Project Introduction and Welcome Message
01:46
Task #1 - Understand the Problem Statement & Business Case
11:13
Task #2 - Import Model with Pre-trained Weights
07:06
Task #3 - Import and Merge Images
09:06
Task #4 - Run the Pre-trained Model and Explore Activations
09:44
Task #5 - Understand the Theory & Intuition Behind Deep Dream Algorithm
19:27
Task #6 - Understand The Gradient Operations in TF 2.0
05:37
Task #7 - Implement Deep Dream Algorithm Part #1
09:10
Task #8 - Implement Deep Dream Algorithm Part #2
10:26
Task #9 - Apply DeepDream Algorithm to Generate Images
06:45
Task #10 - Generate DeepDream Video
07:20

Explainable AI with Zero Coding

5 lectures
Explainable AI Dataset Download & Link to DataRobot
00:08
Project Overview on Food Recognition with AI
07:52
DataRobot Demo 1 - Upload and Explore Dataset
08:44
DataRobot Demo 2 - Train AI/ML Model
05:45
DataRobot Demo 3 - Explainable AI
19:40

Crash Course on AWS, S3, and SageMaker

11 lectures
What is AWS and Cloud Computing?
08:53
Key Machine Learning Components and AWS Tour
09:25
Regions and Availability Zones
06:19
Amazon S3
14:32
EC2 and Identity and Access Management (IAM)
12:41
AWS Free Tier Account Setup and Overview
05:47
AWS SageMaker Overview
09:13
AWS SageMaker Walk-through
10:46
AWS SageMaker Studio Overview
08:41
AWS SageMaker Studio Walk-through
06:59
AWS SageMaker Model Deployment
11:03

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