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!!
Machine Learning Master
Applied ML Master, Deep Learning
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401 Machine learning - introduction
01 Machine learning - introduction
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502 Machine learning - linear prediction
02 Machine learning - linear prediction
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603 Machine learning - Maximum likelihood and linear regression
03 Machine learning - Maximum likelihood and linear regression
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704 Machine learning - Regularization and regression
04 Machine learning - Regularization and regression
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805 Machine learning - regularization, cross-validation and data size
05 Machine learning - regularization, cross-validation and data size
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906 Machine learning - Bayesian learning
06 Machine learning - Bayesian learning
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1007 Machine learning - Bayesian learning part 2
07 Machine learning - Bayesian learning part 2
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1108 Machine learning - Introduction to Gaussian processes
08 Machine learning - Introduction to Gaussian processes
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1209 Machine learning - Gaussian processes
09 Machine learning - Gaussian processes
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1310 Machine learning - Bayesian optimization and multi-armed bandits
10 Machine learning - Bayesian optimization and multi-armed bandits
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1411 Machine learning - Decision trees
11 Machine learning - Decision trees
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1512 Machine learning - Random forests
12 Machine learning - Random forests
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1613 Machine learning - Random forests applications
13 Machine learning - Random forests applications
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1714 Machine learning - Unconstrained optimization
14 Machine learning - Unconstrained optimization
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1815 Machine learning - Logistic regression
15 Machine learning - Logistic regression
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1916 Machine learning - Neural networks
16 Machine learning - Neural networks
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2017 Machine learning - Deep learning I
17 Machine learning - Deep learning I
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2118 Machine learning - Deep learning II, the Google autoencoders and dropout
18 Machine learning - Deep learning II, the Google autoencoders and dropout
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2219 Machine learning - Importance sampling and MCMC I
19 Machine learning - Importance sampling and MCMC I
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2320 Machine learning - Markov chain Monte Carlo (MCMC) II
20 Machine learning - Markov chain Monte Carlo (MCMC) II
Applied ML Master, Reinforcement Learning
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24Introduction to Machine Learning
Introduction to Machine Learning
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25Neural Networks I
Neural Networks I
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26Autodiff
Autodiff
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27Neural Networks II
Neural Networks II
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28Advanced Deep Vision
Advanced Deep Vision
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29RAMP (Practical session) 01
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30RAMP (Practical Session) 02
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31Generative Models I
Generative Models I
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32Generative Models II
Generative Models II
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33Interpretability
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34Theory
Theory
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35Optimization I
Optimization I
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36Optimization II
Optimization II
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37Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs)
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38Language Understanding
Language Understanding
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39Multimodal Learning
Multimodal Learning
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40Computational Neuroscience
Computational Neuroscience
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41Bayesian Neural Nets
Bayesian Neural Nets
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42Deep Learning and Music
Deep Learning and Music
INTEL Machine Learning TOOLKIT
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43Introduction to RL and TD
Introduction to RL and TD
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44Policy Search
Policy Search
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45Batch RL and ADP
Batch RL and ADP
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46Off-Policy Learning
Off-Policy Learning
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47Bandits and Explore-Exploit in RL
Bandits and Explore-Exploit in RL
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48Temporal Abstraction
Temporal Abstraction
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49Multi-task and Transfer in RL
Multi-task and Transfer in RL
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50Deep RL
Deep RL
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51Imitation Learning
Imitation Learning
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52Safety in RL
Safety in RL
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53Multi-agent RL
Multi-agent RL