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Modern Artificial Intelligence Masterclass: Build 6 Projects

Harness the power of AI to solve practical, real-world problems in Finance, Tech, Art and Healthcare
Instructor:
Dr. Ryan Ahmed, Ph.D., MBA
27,211 students enrolled
English [Auto]
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%.” [1] 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 subfield such as machine learning, robotics, and computer vision. For companies to become competitive and skyrocket their growth, they need to leverage Artificial Intelligence (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 [2]. 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. One key unique feature of this course is that we will be training and deploying models using Tensorflow and AWS SageMaker. In addition, we will cover various elements of the AI/ML workflow covering model building, training, hyper-parameters
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’s Colab while leveraging the power of GPUs and TPUs.

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 and Visualize hidden layers using GradCam

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.

Introduction

1
Introduction and Welcome Message
2
Introduction, Key Tips and Best Practices
3
Course Outline and Key Learning Outcomes
4
Get the Materials

Emotion AI

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

AI in Healthcare

1
Project Introduction and Welcome Message
2
Task #1 - Understand the Problem Statement and Business Case
3
Task #2 - Import Libraries and Datasets
4
Task #3 - Visualize and Explore Datasets
5
Task #4 - Understand the Intuition behind ResNet and CNNs
6
Task #5 - Understand Theory and Intuition Behind Transfer Learning
7
Task #6 - Train a Classifier Model To Detect Brain Tumors
8
Task #7 - Assess Trained Classifier Model Performance
9
Task #8 - Understand ResUnet Segmentation Models Intuition
10
Task #9 - Build a Segmentation Model to Localize Brain Tumors
11
Task #10 - Train ResUnet Segmentation Model
12
Task #11 - Assess Trained ResUNet Segmentation Model Performance

AI in Business (Marketing)

1
Project Introduction and Welcome Message
2
Task #1 - Understand AI Applications in Marketing
3
Task #2 - Import Libraries and Datasets
4
Task #3 - Perform Exploratory Data Analysis (Part #1)
5
Task #4 - Perform Exploratory Data Analysis (Part #2)
6
Task #5 - Understand Theory and Intuition Behind K-Means Clustering Algorithm
7
Apply Elbow Method to Find the Optimal Number of Clusters
8
Task #7 - Apply K-Means Clustering Algorithm
9
Task #8 - Understand Intuition Behind Principal Component Analysis (PCA)
10
Task #9 - Understand the Theory and Intuition Behind Auto-encoders
11
Task #10 - Apply Auto-encoders and Perform Clustering

AI In Business (Finance) & AutoML

1
Project Introduction and Welcome Message
2
Notes on Amazon Web Services (AWS)
3
Task #1 - Understand the Problem Statement & Business Case
4
Task #2 - Import Libraries and Datasets
5
Task #3 - Visualize and Explore Dataset
6
Task #4 - Clean Up the Data
7
Task #5 - Understand the Theory & Intuition Behind XG-Boost Algorithm
8
Task #6 - Understand XG-Boost Algorithm Key Steps
9
Task #7 - Train XG-Boost Algorithm Using Scikit-Learn
10
Task #8 - Perform Grid Search and Hyper-parameters Optimization
11
Task #9 - Understand XG-Boost in AWS SageMaker
12
Task #10 - Train XG-Boost in AWS SageMaker
13
Task #11 - Deploy Model and Make Inference
14
Task #12 - Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)

Creative AI

1
Project Introduction and Welcome Message
2
Task #1 - Understand the Problem Statement & Business Case
3
Task #2 - Import Model with Pre-trained Weights
4
Task #3 - Import and Merge Images
5
Task #4 - Run the Pre-trained Model and Explore Activations
6
Task #5 - Understand the Theory & Intuition Behind Deep Dream Algorithm
7
Task #6 - Understand The Gradient Operations in TF 2.0
8
Task #7 - Implement Deep Dream Algorithm Part #1
9
Task #8 - Implement Deep Dream Algorithm Part #2
10
Task #9 - Apply DeepDream Algorithm to Generate Images
11
Task #10 - Generate DeepDream Video

Explainable AI

1
Project Introduction and Welcome Message
2
Introduction and Welcome Message

Crash Course on AWS, S3, and SageMaker

1
What is AWS and Cloud Computing?
2
Key Machine Learning Components and AWS Tour
3
Regions and Availability Zones
4
Amazon S3
5
EC2 and Identity and Access Management (IAM)
6
AWS Free Tier Account Setup and Overview
7
AWS SageMaker Overview
8
AWS SageMaker Walk-through
9
AWS SageMaker Studio Overview
10
AWS SageMaker Studio Walk-through
11
AWS SageMaker Model Deployment
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15 hours on-demand video
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Certificate of Completion
Modern Artificial Intelligence Masterclass: Build 6 Projects
Category:
Data Science
Teacher
Daniel W
Price:
$218.98 $169
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