Andrew Ng’s Machine Learning Specialization 2022 | What is it and is it worth taking?

Step-by-Step Tutorial: Andrew Ng’s Machine Learning Specialization Review

1. Introduction to the Machine Learning Specialization:

• Andrew Ng has launched a new machine learning specialization on Coursera which consists of three courses.
• The specialization covers modern machine learning practices used in Silicon Valley and is a collaboration between deep learning.ai and Stanford University.

2. Course Content Overview:

• The first course introduces supervised machine learning concepts like regression and gradient descent.
• The second course delves into advanced learning algorithms, neural networks, and machine learning application techniques.
• The third course covers unsupervised learning, including clustering, anomaly detection, and deep reinforcement learning.

3. Comparison with the Old Machine Learning Course:

• The new specialization offers more in-depth content compared to the old course.
• It now uses Python for programming assignments and includes interactive graphs for better understanding.
• Andrew Ng's teaching style balances theory with practical implementation, making the concepts easier to grasp.

4. Prerequisites and Course Structure:

• Basic Python programming skills and knowledge of linear algebra are recommended before starting the course.
• Each course in the specialization consists of about 30 hours of material, totaling 90 hours for the entire program.
• The course is self-paced, but it is estimated to take around 6 weeks if you dedicate 15 hours per week to learning.

5. Course Delivery and Tools:

• The specialization is entirely online, with weekly video lectures, quizzes, and Python programming assignments.
• Jupyter notebooks are provided on Coursera's servers, eliminating the need for local installations.
• Libraries used in the course include NumPy, Matplotlib, TensorFlow, and sometimes scikit-learn.

6. Cost and Scholarships:

• The subscription fee for the specialization is around 46 euros per course, totaling under 100 euros for the entire program.
• Coursera offers over 100,000 scholarships, so check if you qualify for financial assistance.
• The real cost of the course lies in the time and effort you invest in learning.

7. Hands-On Coding Assignments:

• The specialization emphasizes hands-on coding assignments where you implement algorithms from scratch using NumPy.
• These assignments provide a deeper understanding of how machine learning algorithms work.

8. Model Explainability and Fairness:

• The course does not cover model explainability and fairness extensively, which are crucial aspects in real-world applications.
• Understanding these concepts is essential to ensure the transparency and fairness of machine learning models.

9. Recommendations and Additional Resources:

• Completing the specialization can add value to your resume and LinkedIn profile, especially if you are pursuing a career in data science or machine learning.
• Supplement your learning with machine learning books like "Hands-On Machine Learning with scikit-learn and TensorFlow" and "The Master Algorithm" by Pedro Domingos.
• Stay updated with the latest trends in machine learning by subscribing to newsletters and engaging in discussions.

10. Conclusion and Next Steps:

• Consider taking the specialization if you want to deepen your knowledge in machine learning or explore a career in data science.
• Use the 7-day free trial to assess if the course aligns with your learning goals.
• Take thorough notes during the course to aid in revision and understanding of complex theories.

By following these steps, you can make the most out of Andrew Ng's Machine Learning Specialization and enhance your skills in the field of machine learning and data science.