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2021-01-25 10:34:10
30-Day Money-Back Guarantee
Development Data Science Machine Learning

Machine Learning MASTER, Zero to Mastery

To Being Machine Learning Mystery
Rating: 4.5 out of 54.5 (102 ratings)
22,094 students
Created by Data Science ACADEMY
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Machine Learning
  • Deep Learning
  • Artifical Intelligence

Course content

5 sections • 64 lectures • 62h 37m total length

  • Preview11:09
  • HOT PROMOTION Calculus MASTER Training
    10:09
  • WATCH THIS
    01:09

  • 01 Machine learning - introduction
    54:39
  • 02 Machine learning - linear prediction
    01:03:06
  • 03 Machine learning - Maximum likelihood and linear regression
    01:14:00
  • 04 Machine learning - Regularization and regression
    01:01:14
  • 05 Machine learning - regularization, cross-validation and data size
    58:41
  • 06 Machine learning - Bayesian learning
    01:17:04
  • 07 Machine learning - Bayesian learning part 2
    21:09
  • 08 Machine learning - Introduction to Gaussian processes
    01:18:54
  • 09 Machine learning - Gaussian processes
    01:17:27
  • 10 Machine learning - Bayesian optimization and multi-armed bandits
    01:20:29
  • 11 Machine learning - Decision trees
    01:06:05
  • 12 Machine learning - Random forests
    01:16:54
  • 13 Machine learning - Random forests applications
    51:35
  • 14 Machine learning - Unconstrained optimization
    01:16:18
  • 15 Machine learning - Logistic regression
    01:13:30
  • 16 Machine learning - Neural networks
    01:04:27
  • 17 Machine learning - Deep learning I
    01:13:13
  • 18 Machine learning - Deep learning II, the Google autoencoders and dropout
    01:12:02
  • 19 Machine learning - Importance sampling and MCMC I
    01:16:17
  • 20 Machine learning - Markov chain Monte Carlo (MCMC) II
    39:16

  • Introduction to Machine Learning
    00:01
  • Neural Networks I
    01:28:29
  • Autodiff
    01:17:56
  • Neural Networks II
    01:34:05
  • Advanced Deep Vision
    01:33:54
  • RAMP (Practical session)
    01:18:45
  • Generative Models I
    01:29:36
  • Generative Models II
    01:21:23
  • Interpretability
    01:19:01
  • Theory
    01:30:51
  • Optimization I
    01:09:46
  • Optimization II
    01:24:15
  • Recurrent Neural Networks (RNNs)
    01:30:39
  • Language Understanding
    01:26:03
  • Multimodal Learning
    01:19:43
  • Computational Neuroscience
    01:34:52
  • Bayesian Neural Nets
    01:31:40
  • Deep Learning and Music
    01:28:58

  • Introduction to RL and TD
    01:29:24
  • Policy Search
    01:29:34
  • Batch RL and ADP
    01:31:51
  • Off-Policy Learning
    01:22:11
  • Bandits and Explore-Exploit in RL
    01:20:53
  • Temporal Abstraction
    01:31:48
  • Multi-task and Transfer in RL
    01:26:17
  • Deep RL
    01:26:23
  • Imitation Learning
    01:05:05
  • Safety in RL
    01:00:38
  • Multi-agent RL
    01:01:37

  • Week1 Introduction to Machine Learning and Toolkit
    08:30
  • Week2 Introduction to Supervised Learning and K Nearest Neighbors
    05:55
  • Week3 Train Test Splits Validation Linear Regression
    06:20
  • Week4 Regularization and Gradient Descent
    06:00
  • Week5 Logistic Regression_Classification Error Metrics_Final
    04:20
  • Week6 Naive Bayes
    05:40
  • Week7 SVM and Kernels
    06:50
  • Week8 Decision Trees
    06:40
  • Week9 Bagging
    04:10
  • Week10 Boosting and Stacking
    05:35
  • Week11 Intro to Unsupervised Learning
    08:30
  • Week12 Dimensionality Reduction
    04:15

Requirements

  • Basci knowledge of computing or programming may be.

Description

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!!

Who this course is for:

  • Computer Science Students
  • Computer Science Graduated
  • Computer Science Master Student
  • Who wanna know Machine Learning

Instructor

Data Science ACADEMY
ML Master Trainer
Data Science ACADEMY
  • 4.6 Instructor Rating
  • 128 Reviews
  • 26,001 Students
  • 2 Courses

Data Science, Machine Learning, Artifical Intelligence, Deep Learning, Search Engine Optimization, Search Engine Marketing, Computatioal Methods and also Python Programming Language Training.

Python, Data Science, Machine Learning, Deep Learning and Artificial Intelligence, we combine and present our lessons with real life examples.

in order to extend the knowledge you have learned beyond the general level of culture.

10+ Years Experience

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