@jmrbug9623
If there is anything I can be truly thankful for, it's education made available for free, especially from MIT.
@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
@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
@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.
@367kkk
I’m thankful that I know English, and I can understand what this man is talking about!
@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,
@aliceinwonderchen2423
Will definitely make a donation to MIT after I got my dream job.
@leonzheng574
I thank MIT, Prof Grimson, Prof Guttag, for the top quality, best and free lectures!
@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 :-)
@kamalkumarmukiri4267
I thank MIT for uploading such a valuable videos to the world.
@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.
@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.
@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.
@TimmmTim
Oh this handsome gentleman taught me Python, thank you MIT for such a great content!
@alienkishorekumar
Prof. Grimson is the best Professor ever.
@aidanjohnwalsh2129
The only reason I'm interested in machine learning is Astrology: a subject that doesn't require anyone's "beliefs" based on severely lacking information and awareness. At least computers are open to learning new things.
@prashantsingh1096
Why I waisted my time on other video, when Prof. Eric is here.I am taking Andrew 's ML course and there I couldn't really understand overfitting and Prof. Eric very simply explains a hard idea with a nice example of Python and Cobra. Love U Prof. Eric :)
@0xLych