Google FREE MACHINE LEARNING COURSE

Google's self-study guide for yearning AI experts includes a progression of exercises with video addresses, certifiable contextual investigations, and active practice works out.

THE CURRICULUM INCLUDES

1. 30+ exercises
2. 25 lessons
3. 15 hours of content
4. Lectures from Google researchers
5. Real-world case studies
6. Interactive visualizations of algorithms

WHAT WILL YOU LEARN?

• How does machine learning differ from traditional programming?
• What is lost, and the way do I measure it?
• How does gradient descent work?
• How would I decide if my model is successful?
• How do I represent my data so a program can leam from it?
• How do I build a deep neural network?

CONTENTS

1. ML Concepts
2. Introduction to ML (3 min)
3. Framing (15 min)
4. Descending into ML (20 min)
5. Reducing Loss (60 min)
6. FirststepswithTF(65mjn)
7. Generalization (15 min)
8. Training and Test Sets (25 min)
9. Validation Set (35 min)
10. Representation (35 min)
11. Feature Crosses (70 min)
12. Regularization: Simplicity (40 min)
13. Logistic Regression (20 min)
14. Classification (90 min)
15. Regularization: Sparsity (20 min)
16. Neural Networks (65 min)
17. Training Neural min)
18. Mufti-Class Neural Nets (45 min)
19. Embeddings (50 min)
20. ML Engineering
21. Production ML Systems (3 min)
22. Static VS. Dynamic Training (7 min)
23. Static vs. Dynamic Inference (7 min)
24. Data Dependencies (14 min)
25. Fairness (70 min)
26. ML Systems within the planet
27. Cancer prediction (5 min)
28. Literature (5 min)
29. Guidelines (2 min)

PREP WORK

Before beginning the Machine Learning curriculum, do the following:

If you're unaccustomed to machine learning, take Introduction to Machine Learning Problem Framing on google. This one-hour self-study course teaches you the way to spot appropriate problems for machine learning.

If you're unaccustomed to NumPy, do the NumPy Ultraquick Tutorial Colab exercise on google, which provides all the Numpy information you wish for this course. If you're unaccustomed to pandas, do the pandas UltraQuick Tutorial Colab exercise on google, which provides all the panda’s information you wish for this course.

PREREQUISITES

Machine Leaming crash program doesn't assume or require any earlier information in AI. However, to grasp the concepts presented and complete the exercises, we recommend that students meet the subsequent prerequisites:

You should be alright with factors, direct conditions, charts of capacities, histograms, and factual means.

You should be an honest programmer. Ideally, you ought to have some experience programming in Python because the programming exercises are in Python.

Notwithstanding, experienced developers without Python experience can normally finish the programming practices in any case. Google provides materials to be told of these concepts. take a look at the pre-requisites tab on the course page to urge these Just search the net for "Google Machine Learning Crash Course" to succeed on the course page.

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