Discover the fundamental concepts behind artificial intelligence (AI) and machine learning in this introductory course. Explore the various types of AI, examine ethical considerations, and delve into the key machine learning models that power modern AI systems. Whether your goal is to work directly with AI, strengthen your software development skills, or enhance your data science expertise, this course provides an essential foundation for success in the field.
What's included
1 video2 readings
Show info about module content
1 video•Total 4 minutes
Welcome Video•4 minutes
2 readings•Total 10 minutes
Welcome•5 minutes
Acknowledgements•5 minutes
Module 1: Introduction to AI
Module 2•2 hours to complete
Module details
What's included
6 videos12 readings5 assignments
Show info about module content
6 videos•Total 18 minutes
Welcome to Module 1•1 minute
1.1 History of AI•4 minutes
1.2 Fitting It Together: AI, ML, and DS•4 minutes
1.3 Current Applications of AI•3 minutes
1.4 Explore Some AI•3 minutes
Video•3 minutes
12 readings•Total 60 minutes
1.1 History of AI•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
1.2 Fitting It Together: AI, ML, and DS•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
1.3 Current Applications of AI•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
1.4 Explore Some AI•5 minutes
Action Point•5 minutes
Stretch Tasks•5 minutes
5 assignments•Total 25 minutes
Graded Questions•5 minutes
1.1 History of AI•5 minutes
1.2 Fitting It Together: AI, ML, and DS•5 minutes
1.3 Current Applications of AI•5 minutes
1.4 Explore Some AI•5 minutes
Module 2: AI and Machine Learning
Module 3•2 hours to complete
Module details
What's included
6 videos11 readings5 assignments
Show info about module content
6 videos•Total 23 minutes
Welcome to Module 2•3 minutes
2.1 Approaches to Machine Learning•4 minutes
2.2 Key Algorithms and Models•5 minutes
2.3 Classifiers•4 minutes
2.4 Linear Regression•4 minutes
Video•2 minutes
11 readings•Total 80 minutes
2.1 Approaches to Machine Learning•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
2.2 Key Algorithms and Models•5 minutes
Action Point•5 minutes
2.3 Classifiers•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
2.4 Linear Regression•30 minutes
Action Point•5 minutes
Stretch Tasks•5 minutes
5 assignments•Total 25 minutes
Graded Questions•5 minutes
2.1 Approaches to Machine Learning•5 minutes
2.2 Key Algorithms and Models•5 minutes
2.3 Classifiers•5 minutes
2.4 Linear Regression•5 minutes
Module 3: What's in the Black Box? Deep Learning and Neural Networks
Module 4•2 hours to complete
Module details
What's included
6 videos10 readings5 assignments
Show info about module content
6 videos•Total 19 minutes
Welcome to Module 3•2 minutes
3.1 Introduction to Neural Networks•5 minutes
3.2 Neural Network Layers•4 minutes
3.3 Introducing the MNIST Database•3 minutes
3.4 Training your Neural Network•4 minutes
Video•2 minutes
10 readings•Total 50 minutes
3.1 Introduction to Neural Networks•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
3.2 Neural Network Layers•5 minutes
Action Point•5 minutes
3.3 Introducing the MNIST Database•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
3.4 Training your Neural Network•5 minutes
Critical Thinking•5 minutes
5 assignments•Total 25 minutes
Graded Questions•5 minutes
3.1 Introduction to Neural Networks•5 minutes
3.2 Neural Network Layers•5 minutes
3.3 Introducing the MNIST Database•5 minutes
3.4 Training your Neural Network•5 minutes
Module 4: Training and Evaluating Models
Module 5•2 hours to complete
Module details
What's included
7 videos12 readings6 assignments
Show info about module content
7 videos•Total 22 minutes
Welcome to Module 4•2 minutes
4.1 Training, Validation and Test Data•3 minutes
4.2 Overfitting and Underfitting•4 minutes
4.3 Loss Functions and Optimizers•3 minutes
4.4 More use of MNIST•3 minutes
4.5 Power Consumption, Performance, and Sustainability•3 minutes
Video•2 minutes
12 readings•Total 85 minutes
4.1 Training, Validation and Test Data•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
Action Point•5 minutes
4.3 Loss Functions and Optimizers•5 minutes
Critical Thinking•5 minutes
4.4 More use of MNIST•5 minutes
Action Point•30 minutes
4.5 Power Consumption, Performance, and Sustainability•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
Stretch Tasks•5 minutes
6 assignments•Total 35 minutes
Graded Questions•5 minutes
4.1 Training, Validation and Test Data•5 minutes
4.2 Overfitting and Underfitting•5 minutes
4.3 Loss Functions and Optimizers•5 minutes
4.4 More use of MNIST•5 minutes
4.5 Power Consumption, Performance, and Sustainability•10 minutes
Module 5: Advanced Topics in AI
Module 6•2 hours to complete
Module details
What's included
6 videos11 readings5 assignments
Show info about module content
6 videos•Total 18 minutes
Welcome to Module 5•2 minutes
5.1 Introduction to CNNs and RNNs•4 minutes
5.2 Introduction to the PyTorch Framework•3 minutes
5.3 Linear Number Relationships•3 minutes
5.4 Text Recognition•3 minutes
Video•2 minutes
11 readings•Total 55 minutes
5.1 Introduction to CNNs and RNNs•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
5.2 Introduction to the PyTorch Framework•5 minutes
Action Point•5 minutes
5.3 Linear Number Relationships•5 minutes
Critical Thinking•5 minutes
Action Point•5 minutes
5.4 Text Recognition•5 minutes
Action Point•5 minutes
Stretch Tasks•5 minutes
5 assignments•Total 45 minutes
Graded Questions•5 minutes
5.1 Introduction to CNNs and RNNs•10 minutes
5.2 Introduction to the PyTorch Framework•10 minutes
5.3 Linear Number Relationships•10 minutes
5.4 Text Recognition•10 minutes
Module 6: Ethics, Challenges, and the Future of AI
Arm technology is defining the future of computing. Our energy-efficient processor designs and software platforms have enabled advanced computing in more than 225 billion chips and our technologies securely power products from the sensor to the smartphone and the supercomputer.
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Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.