Gururaj Surampalli: MACHINE LEARNING

MACHINE LEARNING

AI-ML-MTECH-----https://gururajsurampalli.blogspot.com/p/mtech-ai-ml.html

Data Visualization Using Python: https://gururajsurampalli.blogspot.com/p/data-visualisation-using-python.html

Watch This space for more useful things about ML
5skills for ml
Credit: https://youtu.be/DZ7xuZ1-uh8
Let's see what are the top 5 skills to get into machine learning profession.
1). At number 1, we have Math Skills: Under math skills, we need to know probability and statistics, linear algebra and calculus. Probability and Statistics: Machine learning is very much closely related to statistics. You need to know the fundamentals of statistics and probability theory, descriptive statistics, Baye's rule, and random variables, probability distributions, sampling, hypothesis testing, regression, and decision analysis. Linear Algebra: You need to know how to with matrices and some basic operations on matrices such as matrix addition, subtraction, scalar, and vector multiplication, inverse, transpose and vector spaces. Calculus: In calculus, you need to know the basics of differential and integral calculus. 2). At number two we have Programming skills: A little bit of coding skills is enough. But it's preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts. Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++. It's your preference to master any one programming language. But it is advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one. 3). At number 3 we have Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing, the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations, data analysis and visualization skills. 4). Next, we have Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning algorithms such as linear regression, logistic regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks. 5). And Finally, The Knowledge of Machine Learning Frameworks: You Should be Familiar with popular machine learning frameworks such as sci-kit learn, tensorflow, Azure, caffe, theano, spark, and torch.

MODULE 1 PPTs: https://www.slideshare.net/secret/yMWDyV07EdlIPV
Checkers Game: https://youtu.be/AsnuPjQvlro

http://jmvidal.cse.sc.edu/talks/conceptlearning/index.xml?style=White

FindS algorithm: https://youtu.be/s-vZplHyETc

Some other Data sets for reference

b)
+ ((male brown tall US) (female black short US))
+ ((male brown short French)( female black short US))
- ((female brown tall German)( f emale black short Indian))
+ ((male brown tall Irish) (female brown short Irish))

Some problems and solutions: click here https://drive.google.com/file/d/1cfwZj5TRAahSjprBO4AlCcdnk2NZO7g6/view?usp=sharing

Shesharao sir teaching- https://drive.google.com/file/d/1-j60srcP1dlo8wZKilbxEt__gxLl-B_B/view?usp=drivesdk

Module 2 PPT: https://drive.google.com/file/d/1xHYL2ba6puygAv4R8ShAbTwOnHuSYe1e/view?usp=sharing

Programs: https://deepakdvallur.weebly.com/machine-learning-laboratory.html
Calculator: https://web2.0calc.com/

EXAMPLE DT: DRAW Decision Tree

EXAMPLE:For the following dataset, find the partial decision tree. [Nodes in Level 1 (i.e. root)

and Level 2 only]. Last column represents the label.

QP_dec18_jan19

QP_JULY_2019 (16 MARKS)

DAY	A1	A2	A3	CLASSIFICATION
1	TRUE	HOT	HIGH	NO
2	TRUE	HOT	HIGH	NO
3	FALSE	HOT	HIGH	YES
4	FALSE	COOL	NORMAL	YES
5	FALSE	COOL	NORMAL	YES
6	TRUE	COOL	HIGH	NO
7	TRUE	HOT	HIGH	NO
8	TRUE	HOT	NORMAL	YES
9	FALSE	COOL	NORMAL	YES
10	FALSE	COOL	HIGH	NO