A mini course to help you pass Ng's Stanford University Machine learning Course


    
My name is Napoleon Boakye and I am a mentor and a machine learning Instructor at Youth Escape Arena, Inc. where I teach machine learning course to beginners. There is also a slack group of current Machine Learning(ML) scholars that I am part of that assist and mentor students of Machine Learning. Send me your email and I will invite you to join the group, that is where most communication between students and mentors takes place. You will find questions earlier beginners came up with that has been answered by mentors week by week. Some are Indian speaking learners, others are Chinese, Spanish, etc.. speaking learners too. They will be your mentor and assist you in any way shape or form. Most of them are from Ng's Machine Learning Course. 

    We choose Ng's Machine Learning course to mentor because it is the best and cheapest Machine Learning course of the century for beginners. Not only is it a beginners course but it is also better than Udacity's Machining Learning Course. It also presents Machine Learning understanding on a platter plate for you so that you can solve your own problems too. His lectures are in its simplest form and this free mentorship takes you to the antecedents of Machine Learning understanding and support you to grow in ML and solve today's problems. In this mentorship, we pay special attention to your understanding and how you can pass the Ng's course with flying colors. So if you haven't registered yet, register now or lets start with week 1 and 2 and reduce the lessons down to the core.

By the way, all credits goes to Stanford University and Coursera for providing affordable course to the world and we pray that they keep bringing more courses online for the world. Don't forget to send me an email at napoleon@machinelearningmentor.com so that I can send you the link to over 200 mentors from all over the world in a private slack group of Machine Learning Scholars. Lets get our hands dirty with week 1 and 2. But before listen to this pod cast to see what ML can do for mankind.


Content

  • 01 and 02: Introduction, Regression Analysis and Gradient Descent: Week 1 and 2 

  • 03: Linear Algebra - review: Week 3
  • 04: Linear Regression with Multiple Variables: Week 4
  • 05: Octave[incomplete: Week 5
  • 06: Logistic Regression: Week 6
  • 07: Regularization: Week 7
  • 08: Neural Networks - Representation: Week 8
  • 09: Neural Networks - Learning: Week 9
  • 10: Advice for applying machine learning techniques: Week 10
  • 11: Machine Learning System Design: Week 11
  • 12: Support Vector Machines: Week 12
  • 13: Clustering: Week 13
  • 14: Dimensionality Reduction: Week 11
  • 15: Anomaly Detection: Week 15
  • 16: Recommender Systems: Week 16
  • 17: Large Scale Machine Learning: Week 17
  • 18: Application Example - Photo OCR: Week 18
  • 19: Course Summary: Week 19