Modern Artificial Intelligence with Zero Coding

Rating 4.59 out of 5 (738 ratings in Udemy)

What you'll learn

Build, train and deploy AI models to detect people emotions using Google Teachable Machine
Explain the difference between learning rate, epochs, batch size, accuracy and loss.
Predict Insurance Premium using Customer Features such as age, smoking habit and geo-location using AWS AI AutoPilot
Build, train and deploy advanced AI to detect cardiovascular disease using DataRobot AI
Leverage the power of AI to recognize food types …

Rating 4.59 out of 5 (738 ratings in Udemy)

What you'll learn

Build, train and deploy AI models to detect people emotions using Google Teachable Machine
Explain the difference between learning rate, epochs, batch size, accuracy and loss.
Predict Insurance Premium using Customer Features such as age, smoking habit and geo-location using AWS AI AutoPilot
Build, train and deploy advanced AI to detect cardiovascular disease using DataRobot AI
Leverage the power of AI to recognize food types using DataRobot AI
Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines
Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate
List the various advantages of transfer learning and know when to properly apply the technique to speed up training process
Understand the theory and intuition behind residual networks, a state-of-the-art deep neural networks that are widely adopted in business, and healthcare
Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in DataRobot
Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity)
Learn how to use SageMaker Studio AutoML tool to build, train and deploy AI/Ml models which requires almost zero coding experience
Differentiate between various regression models KPIs such as R2 or coefficient of determination, Mean absolute error, Mean Squared error, and Root Mean Squared Error
Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot

Description

Do you want to build super-powerful applications in Artificial intelligence (AI) but you don’t know how to code?

Are you intimidated by AI and don’t know where to start?

Or maybe you don’t have a computer science degree and want to break into AI?

Are you an aspiring entrepreneur who wants to maximize business revenue and reduce costs with AI but don’t know how to get there quickly and efficiently?

If the answer is yes to any of these questions, then this course is for you!

Artificial intelligence is one of the top tech fields to be in right now!

AI will change our lives in the same way electricity did 100 years ago.

AI is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospects.

This course solves a key problem which is making AI available to anyone with no coding background or computer science degree.

The purpose of this course is to provide you with knowledge of key aspects of modern AI without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.

In this course, we will assume that you have been recently hired as a consultant at a start-up in San Francisco. The CEO has tasked you to apply cutting-edge AI techniques to 5 projects. There is only one caveat, your key data scientist quit on you and do not know how to code, and you need to generate results fast. In fact, you only have one week to solve these key company problems. You will be provided with datasets from all these departments and you will be asked to achieve the following tasks:

Project #1: Develop an AI model to detect people's emotions using Google Teachable Machines (Technology).
Project #2: Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines (HealthCare).
Project #3: Predict Insurance Premium using Customer Features such as age, smoking habit, and geo-location using AWS AI AutoPilot (Business).
Project #4: Detect Cardiovascular Disease using DataRobot AI (HealthCare).
Project #5: Recognize food types and explore AIexplainability using DataRobot AI (Technology).

Duration 9 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

7097

Rating 4.59 out of 5 (738 ratings in Udemy)

Go to the Course
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