ai lecture

media/Machine Learning Explained in 100 Seconds [PeMlggyqz0Y].tsv

@@ -0,0 +1,31 @@
+start	end	text
+0	6480	 Machine learning. Teach a computer how to perform a task, without explicitly programming it to perform said task.
+6620	13420	 Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns.
+13580	20400	 The term was coined in 1959 by Arthur Samuel at IBM, who was developing artificial intelligence that could play checkers.
+20540	26880	 Half a century later, and predictive models are embedded in many of the products we use every day, which perform two fundamental jobs.
+26880	32040	 One is to classify data, like "Is there another car on the road?" or "Does this patient have cancer?"
+32040	38600	 The other is to make predictions about future outcomes, like "Will this stock go up?" or "Which YouTube video do you want to watch next?"
+38600	43280	 The first step in the process is to acquire and clean up data. Lots and lots of data.
+43480	47780	 The better the data represents the problem, the better the results. Garbage in, garbage out.
+47900	52160	 The data needs to have some kind of signal to be valuable to the algorithm for making predictions.
+52160	59920	 And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem.
+60240	64240	 The next step is to separate the data into a training set and testing set.
+64460	71800	 The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model.
+71980	77700	 The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression,
+77940	81260	 or a decision tree that assigns different weights to features in the data.
+81260	86640	 Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns
+86640	91300	 weights to features, but also takes the input data and creates additional features automatically.
+91640	96300	 And that's extremely useful for datasets that contain things like images or natural language,
+96420	99020	 where manual feature engineering is virtually impossible.
+99260	103960	 Every one of these algorithms learns to get better by comparing its predictions to an error function.
+104160	109840	 If it's a classification problem, like "Is this animal a cat or a dog?" the error function might be accuracy.
+109840	115900	 If it's a regression problem, like "How much will a loaf of bread cost next year?" then it might be mean absolute error.
+116220	121780	 Python is the language of choice among data scientists, but R and Julia are also popular options,
+121920	125320	 and there are many supporting frameworks out there to make the process approachable.
+125500	132680	 The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on,
+132860	136900	 then spits out a prediction that tries to minimize the error that it was optimized for.
+136900	141980	 It can then be embedded on an actual device or deployed to the cloud to build a real-world product.
+142180	144500	 This has been Machine Learning in 100 Seconds.
+144580	147160	 Like and subscribe if you want to see more short videos like this,
+147320	150500	 and leave a comment if you want to see more machine learning content on this channel.
+150620	153040	 Thanks for watching, and I will see you in the next one.