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Artificial Intelligence



Machine Learning 



Data Science
- Ashutosh Trivedi
Artificial Intelligence



For a machine to be Intelligent, It should have:
- Vision
- Language Understanding

- Speech processing
- Planning
- Ability to learn all of above 

Computer Vision
- Edge detection
- Face recognition
- Image Identification (classification)
- Scene Description
- object detection
Language Understanding
(Natural language Processing)
- Grammar, Part of Speech,Named Entity Recognition(NER)
- Natural Language classification (sentiment detection )
- Translation
- Speech synthesis
Machine Learning



Learning Algorithms
How do we learn from -
- We learn from data. Identify pattern.
- Store that pattern which is called a model
Machine Learning



Data and Statistics -
- Many methods of ML and Statistics are overlapped
- PAC Learning ( probabilistic approximately correct learning )
Probability distributions
Mean, Covariance
Random variables
Regression Analysis
Prerequisites -
Probability theory -
Basics of Probability
Advance topics of probability
Populis Probability Theory(book)
Linear Algebra -
The learning algorithms are linear algebra operations
- Singular value decompositions
- Matrix factorizations etc.
Gilbert Strand Linear Algebra
Implementation in Python -
Numpy
Scipy
Watch Pycon, Scipy videos on youTube ( channel )
Basics of Numpy
Machine Learning -
Andrew Ng’s Machine Learning course
Stanford Lectures
coursera course
Neural Networks -
Unstructured data - images/ videos / text
Best book to start with
A very serious book
Implementation in Python -
Scikit- Learn
TensorFlow
Pytorch
caffe
Projects to follow
OpenAI
Data Science
Everything which is related to getting intelligence out of
data comes under data science
Structured
Unstructured
Small data
Big data
What is Apache Spark/ hadoop?
Apache Spark Course
Create an account on Github. Start following projects there! Learn from the code.
Follow people on GitHub.

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Ml basics

  • 1. Artificial Intelligence
 
 Machine Learning 
 
 Data Science - Ashutosh Trivedi
  • 2. Artificial Intelligence
 
 For a machine to be Intelligent, It should have: - Vision - Language Understanding
 - Speech processing - Planning - Ability to learn all of above 

  • 3. Computer Vision - Edge detection - Face recognition - Image Identification (classification) - Scene Description - object detection Language Understanding (Natural language Processing) - Grammar, Part of Speech,Named Entity Recognition(NER) - Natural Language classification (sentiment detection ) - Translation - Speech synthesis
  • 4. Machine Learning
 
 Learning Algorithms How do we learn from - - We learn from data. Identify pattern. - Store that pattern which is called a model
  • 5. Machine Learning
 
 Data and Statistics - - Many methods of ML and Statistics are overlapped - PAC Learning ( probabilistic approximately correct learning ) Probability distributions Mean, Covariance Random variables Regression Analysis
  • 6. Prerequisites - Probability theory - Basics of Probability Advance topics of probability Populis Probability Theory(book) Linear Algebra - The learning algorithms are linear algebra operations - Singular value decompositions - Matrix factorizations etc. Gilbert Strand Linear Algebra Implementation in Python - Numpy Scipy Watch Pycon, Scipy videos on youTube ( channel ) Basics of Numpy
  • 7. Machine Learning - Andrew Ng’s Machine Learning course Stanford Lectures coursera course Neural Networks - Unstructured data - images/ videos / text Best book to start with A very serious book Implementation in Python - Scikit- Learn TensorFlow Pytorch caffe Projects to follow OpenAI
  • 8. Data Science Everything which is related to getting intelligence out of data comes under data science Structured Unstructured Small data Big data What is Apache Spark/ hadoop? Apache Spark Course Create an account on Github. Start following projects there! Learn from the code. Follow people on GitHub.