@0xLych

MIT classes, available for all, free for all, anywhere around the world. What a time to be alive.

@jmrbug9623

If there is anything I can be truly thankful for, it's education made available for free, especially from MIT.

@wiktormichalec5639

I thank MIT for uploading such a valuable videos to the world.
Will definitely make a donation to MIT after I got my dream job.
I can listen to this teacher 100 hours continuously. the way he spread words and his character.

@nicolareiman9687

I can listen to this teacher 100 hours continuously. the way he spread words and his character.

@leixun

My takeaways:
1. What is machine learning 8:13
2. How are things learned 9:55
3. Supervised learning and unsupervised learning 13:55
4. Clustering 15:01
5. Feature engineering 25:35
6. Minkowski metric: Manhattan distance and Euclidean distance 34:19
7. Classification example 43:42
- Confusion matrix and accuracy 46:50
- Other measurements: positive predictive value, sensitivity and specificity 49:25

@pradeepsasindu2023

Timestamps (Powered by Merlin AI)
00:03 - Introduction to linear regression and preparing for machine learning topics
02:25 - Introduction to basic concepts of machine learning
06:33 - Machine learning enables computers to learn without explicit programming
08:40 - Introduction to Machine Learning process and algorithms
13:05 - Introduction to supervised and unsupervised learning in Machine Learning
14:59 - Machine learning involves learning to distinguish between classes based on data characteristics.
18:53 - Identifying and separating data points based on known labels
20:36 - Machine learning involves labeling data for classification purposes.
24:26 - Introduction to feature engineering in machine learning.
26:15 - Selecting relevant features is crucial for accurate predictions.
29:44 - Identifying animal types based on features
31:40 - Importance of minimizing false negatives in classification
35:22 - Different distance metrics affect the perception of closeness between points
37:19 - Feature engineering is crucial in classification.
40:48 - Machine learning involves measuring distance between examples and deciding on the right features and constraints for the model.
42:42 - Discussion on determining optimal number of clusters
46:03 - Evaluating classifiers using confusion matrix
47:46 - Understanding the concept of true positives, true negatives, false positives, and false negatives in machine learning

@ppvshenoy

This course is the BEST I’ve ever seen/heard explaining the concepts of Machine Learning/Deep Learning. Just simply awesome. MIT students are really lucky  to have this professor. Makes me wish to be a college student again :-)

@leonzheng574

I thank MIT, Prof Grimson, Prof Guttag, for the top quality, best and free lectures!

@kamalkumarmukiri4267

I thank MIT for uploading such a valuable videos to the world.

@jameswharton5259

I wanted to express my gratitude for the detailed and engaging content. Thank you for breaking down complex concepts into digestible parts and providing clear examples. This lecture has significantly deepened my understanding of machine learning principles and practices.

@367kkk

I’m thankful that I know English, and I can understand what this man is talking about!

@aliceinwonderchen2423

Will definitely make a donation to MIT after I got my dream job.

@zAbdullahKhan

A phenomenal professor who also makes learning fun. This is lecture number 11, which means that there are more lectures under this course, but I couldn't find the entire playlist on the channel.

@MelikBaykul

In my opinion, this video about first week in a term. I can't share my machine learning knowledge here. He is right, I think, he wants to say that " if you want to learn machine learning from mit, first of all, you should get an acceptance, after that, you have to attend the courses. when you study hard, you can pass the exam." 
Best,

@Speed001

Sounds related to how we developed probability and statistics sums and formulas

@kwubegharitony2044

Brilliant teachers in MIT. I envy the students in this department.

@TimmmTim

Oh this handsome gentleman taught me Python, thank you MIT for such a great content!

@alienkishorekumar

Prof. Grimson is the best Professor ever.

@mehmetcemunal

good algo👍

@naipahalcareerinstitute9100

Sir good evening , I am not related to this subject but your way of explaining subject matter is excellent .You are lecturer from top most institute in the world so I love to watch way of teaching process , methods and dealing with subject topics.your attitude and way of dealing contents is Very simple.Congratulations sir..