Machine Learning MASTER, Zero to Mastery
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Machine Learning MASTER
To being Machine Learning Mystery
I am sure a number of you have heard about machine learning. A dozen of you might even know what it is. And a couple of you might have worked with machine learning algorithms too.
You see where this is going? Not a lot of people are familiar with the technology that will be absolutely essential 5 years from now. Siri is machine learning. Amazon’s Alexa is machine learning. Ad and shopping item recommender systems are machine learning.
Let’s try to understand machine learning with a simple analogy of a 2 year old boy. Just for fun, let’s call him Kylo Ren
Let’s assume Kylo Ren saw an elephant. What will his brain tell him ?(Remember he has minimum thinking capacity, even if he is the successor to Vader). His brain will tell him that he saw a big moving creature which was grey in color. He sees a cat next, and his brain tells him that it is a small moving creature which is golden in color. Finally, he sees a light saber next and his brain tells him that it is a non-living object which he can play with!
His brain at this point knows that saber is different from the elephant and the cat, because the saber is something to play with and doesn’t move on its own. His brain can figure this much out even if Kylo doesn’t know what movable means. This simple phenomenon is called Clustering .
Machine learning is nothing but the mathematical version of this process.
A lot of people who study statistics realized that they can make some equations work in the same way as brain works.
Brain can cluster similar objects, brain can learn from mistakes and brain can learn to identify things.
All of this can be represented with statistics, and the computer based simulation of this process is called Machine Learning. Why do we need the computer based simulation? because computers can do heavy math faster than human brains.
I would love to go into the mathematical/statistical part of machine learning but you don’t wanna jump into that without clearing some concepts first.
Let’s get back to Kylo Ren. Let’s say Kylo picks up the saber and starts playing with it. He accidentally hits a stormtrooper and the stormtrooper gets injured. He doesn’t understand what’s going on and continues playing. Next he hits a cat and the cat gets injured. This time Kylo is sure he has done something bad, and tries to be somewhat careful. But given his bad saber skills, he hits the elephant and is absolutely sure that he is in trouble.
He becomes extremely careful thereafter, and only hits his dad on purpose as we saw in Force Awakens!!
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401 Machine learning - introductionVideo lesson
01 Machine learning - introduction
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502 Machine learning - linear predictionVideo lesson
02 Machine learning - linear prediction
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603 Machine learning - Maximum likelihood and linear regressionVideo lesson
03 Machine learning - Maximum likelihood and linear regression
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704 Machine learning - Regularization and regressionVideo lesson
04 Machine learning - Regularization and regression
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805 Machine learning - regularization, cross-validation and data sizeVideo lesson
05 Machine learning - regularization, cross-validation and data size
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906 Machine learning - Bayesian learningVideo lesson
06 Machine learning - Bayesian learning
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1007 Machine learning - Bayesian learning part 2Video lesson
07 Machine learning - Bayesian learning part 2
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1108 Machine learning - Introduction to Gaussian processesVideo lesson
08 Machine learning - Introduction to Gaussian processes
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1209 Machine learning - Gaussian processesVideo lesson
09 Machine learning - Gaussian processes
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1310 Machine learning - Bayesian optimization and multi-armed banditsVideo lesson
10 Machine learning - Bayesian optimization and multi-armed bandits
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1411 Machine learning - Decision treesVideo lesson
11 Machine learning - Decision trees
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1512 Machine learning - Random forestsVideo lesson
12 Machine learning - Random forests
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1613 Machine learning - Random forests applicationsVideo lesson
13 Machine learning - Random forests applications
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1714 Machine learning - Unconstrained optimizationVideo lesson
14 Machine learning - Unconstrained optimization
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1815 Machine learning - Logistic regressionVideo lesson
15 Machine learning - Logistic regression
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1916 Machine learning - Neural networksVideo lesson
16 Machine learning - Neural networks
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2017 Machine learning - Deep learning IVideo lesson
17 Machine learning - Deep learning I
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2118 Machine learning - Deep learning II, the Google autoencoders and dropoutVideo lesson
18 Machine learning - Deep learning II, the Google autoencoders and dropout
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2219 Machine learning - Importance sampling and MCMC IVideo lesson
19 Machine learning - Importance sampling and MCMC I
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2320 Machine learning - Markov chain Monte Carlo (MCMC) IIVideo lesson
20 Machine learning - Markov chain Monte Carlo (MCMC) II
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24Introduction to Machine LearningText lesson
Introduction to Machine Learning
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25Neural Networks IVideo lesson
Neural Networks I
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26AutodiffVideo lesson
Autodiff
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27Neural Networks IIVideo lesson
Neural Networks II
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28Advanced Deep VisionVideo lesson
Advanced Deep Vision
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29RAMP (Practical session) 01Video lesson
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30RAMP (Practical Session) 02Video lesson
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31Generative Models IVideo lesson
Generative Models I
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32Generative Models IIVideo lesson
Generative Models II
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33InterpretabilityVideo lesson
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34TheoryVideo lesson
Theory
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35Optimization IVideo lesson
Optimization I
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36Optimization IIVideo lesson
Optimization II
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37Recurrent Neural Networks (RNNs)Video lesson
Recurrent Neural Networks (RNNs)
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38Language UnderstandingVideo lesson
Language Understanding
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39Multimodal LearningVideo lesson
Multimodal Learning
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40Computational NeuroscienceVideo lesson
Computational Neuroscience
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41Bayesian Neural NetsVideo lesson
Bayesian Neural Nets
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42Deep Learning and MusicVideo lesson
Deep Learning and Music
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43Introduction to RL and TDVideo lesson
Introduction to RL and TD
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44Policy SearchVideo lesson
Policy Search
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45Batch RL and ADPVideo lesson
Batch RL and ADP
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46Off-Policy LearningVideo lesson
Off-Policy Learning
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47Bandits and Explore-Exploit in RLVideo lesson
Bandits and Explore-Exploit in RL
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48Temporal AbstractionVideo lesson
Temporal Abstraction
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49Multi-task and Transfer in RLVideo lesson
Multi-task and Transfer in RL
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50Deep RLVideo lesson
Deep RL
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51Imitation LearningVideo lesson
Imitation Learning
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52Safety in RLVideo lesson
Safety in RL
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53Multi-agent RLVideo lesson
Multi-agent RL
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