@AdityaChavhan-z7j
If you are using this time stamp then provide like. =======Timestamps==== 00:04 - Artificial Intelligence (AI) 00:37 - Machine Learning 01:30 - Algorithm 02:06 - Data 02:48 - Model 03:30 - Model fitting 03:44 - Training Data 04:17 - Test Data 04:54 - Supervised Learning 05:24 - Unsupervised Learning 06:01 - Reinforcement Learning 07:05 - Feature (Input, Independent Variable, Predictor) 07:45 - Feature engineering 08:15 - Feature Scaling (Normalization, Standardization) 08:48 - Dimensionality 09:34 - Target (Output, Label, Dependent Variable) 09:59 - Instance (Example, Observation, Sample) 10:32 - Label (class, target value) 11:16 - Model complexity 12:15 - Bias & Variance 13:23 - Bias Variance Tradeoff 14:11 - Noise 14:30 - Overfitting & Underfitting 15:20 - Validation & Cross Validation 16:20 - Regularization 16:40 - Batch, Epoch, Iteration 17:40 - Parameter 18:22 - Hyperparameter 18:50 - Cost Function (Loss Function, Objective Function) 19:39 - Gradient Descent 20:49 - Learning Rate 21:28 - Evaluation
@vitorgoncalves4992
This is a vídeo that anyone should watch before they start their journey on machine Learning. I have already studied a lot of what has been covered here, but it they are all split in different courses. It was never this clear to me how connected or not they are to each other before watching this.
@sitrakaforler8696
Hooooo It is crazy for I'm preparing a course and dude. You are one of the best teacher of internet ! It is so crazy that you don't have more subs but trust me I am and I will share your chanel as much as i can !!!!
@NitinKumar-hw2ps
Love the content, conciseness and clarity. Would love a max 2-3 hr session with sections to directly go to each of them, to learn Machine Learning.
@divyanshpandey8355
Hi, I subbed you 3 days ago, here to take the credit of being with you from start before you get to the top. Cheers!
@BengtFrost
Great condensed refresher! Very clear!
@josephthehansen
Incredibly well made and informative, this channel is a goldmine. Would love to have a video covering more advanced concepts like activation functions, transformers, autoencoders, etc.
@billmichae
Nice presentation. Just a small correction: Data are not information!
@SabrinaXe
8:01 feature engineering very important. Can distinguish a good ML model from a poor model. It helps model focus on relevant patterns. Ex: instead of date like 17/05/2023 we can create features as Day of week Holiday? In ML we have to do feature engineering but in deep learning no need to do feature selection or engineering 9:10 dimension reduction done because of curse of dimensionality-as features and data grows, it grows sparse and patterns are harder to find 11:16 model complexity like linear model too simple because only 2 parameters 12:31 as we increase the polynomial from linear to quadratic for ex, it can fit curves Undercutting is called bias because it doesn’t accurately capture the pattern. It generalises the pattern hence it’s biased. Too general like stereotypes. Hence fails to capture complex patterns Bias happens when for example we use a linear model to model relationships that are not linear Variance (overfitting) is also bad because it may capture noise in data as patterns. The ML model learns the noise. Variance bad with new test data. 13:27 bias variance trade off like not linear model but neither too complex with high polynomials
@yomonsbuzz4978
Great just what i was looking for
@daudahmad112
Thank you Mr. Arnold Schwarznegger, the refresher was awesome!
@ShivaniSharma-ib7sb
Nice video!! A few months ago, my son, who's learning both machine learning and game development at moonpreneur, realized that the future of tech isn't just about coding anymore. He’s now focusing on how AI can enhance user experience and optimize content. His program’s shift towards AI management roles and creativity, which aligns with his evolving skills. It’s exciting to see how AI is shaping career paths and offering new opportunities for the next generation!
@saisrikaranpulluri1472
Amazing, though I couldn’t grab all the information. Your video is very informative and helpful. Thank you so much.
@whiteduck5563
Your channel is very informative. I'm actually learning ML by the steps you laid out in your other video.
@RobertHorvat93
your videos are amazing, keep them coming :D
@BhavyaKhandelwal-ci7fs
Watching this as a recap before my interview.
@rymfouzari792
This is soooo useful,thank you so much !!
@AlishaKatyayani
This is a really nice video to watch before getting your hands dirty with "Actual Stuff"
@abhishekkumarmahato6413
Excellent video! Although I think it's certainly hard to get all this knowledge stored in the brain in just 22 mins if you don't have a pretty good background earlier, nonetheless I find it an excellent source to understand the ML concepts all in one.
@arpitsinghal07