Power point presentation on AI, AI Models, Tools, Types of AI, AI projects, AI Technologies

REFRESHER COURSE IN COMPUTER SCIENCE YOUTUBE LINKS

 CLOUD

https://www.youtube.com/watch?v=M-13NVkTTzM

https://www.youtube.com/watch?v=Qb6L9i2b0sk

https://www.youtube.com/watch?v=5q7-Rf9y2lQ

https://drive.google.com/file/d/1Et39IY48XjLHmSRPQxIe1ghBuSkZVkVc/view

https://www.youtube.com/watch?v=eEQg_m_lXVo

AUGMENTED REALITY :

https://www.youtube.com/watch?v=3oOSYfMDVFM&t=3s

https://www.youtube.com/watch?v=gVB97H_VP5s

nlp

https://www.youtube.com/watch?v=0NkSPJUBwJo

https://www.youtube.com/watch?v=3IAmlWhpWtc

https://www.youtube.com/watch?v=w17hhPAXdas

IOT:

https://www.youtube.com/watch?v=YR1zKl6tnrM

ml

https://www.youtube.com/watch?v=eZDAZcXPJ0U

https://www.youtube.com/watch?v=q14CQPkxdt4

https://www.youtube.com/watch?v=VCDb0MHTM7Y

https://www.youtube.com/watch?v=omeEjvwjh3g

https://www.youtube.com/watch?v=fSpXmdTHNZs

BLOCK CHAIN

https://www.youtube.com/watch?v=dBOeXgi_JFk

QUANTING COMPUTING

https://www.youtube.com/watch?v=T8NebqqHLW0

CYBER PANDEMIC

https://www.youtube.com/watch?v=qA9Sg9DWdgA

https://www.youtube.com/watch?v=i97bw12FkRE

M.Sc Computer Science Krishna University Data Science Notes

life cycle of a thread notes

 A thread life cycle is always in one of these five states. It can move from one state to another state. In Java, the life cycle of thread has five states.

1. Newborn State

2. Runnable State

3. Running State

4. Blocked State

5. Dead State

1. New : A thread begins its life cycle in the new state. It remains in this state until the start() method is called on it.
   Thread t = new Thread();

2. Runnable : After invocation of start() method on new thread, the thread becomes runnable.
   Thread t = new Thread();
   t.start();

3. Running : A thread is in running state if the thread scheduler has selected it.

t1.isAlive() - The isAlive() method returns true if the thread upon which it is called is still running otherwise it returns false.

4. Waiting : A thread is in waiting state if it waits for another thread to perform a task. In this stage the thread is still alive.

Example1:   t1.join() waiting for t1 to complete its execution

Example2:   The state of thread t1 after invoking the method sleep() on it - TIMED_WAITING

Thread.sleep(400);

5. Terminated : A thread enters the terminated state when it completes its task.

t1.stop()

Deadlock Recovery

 deadlock recovery techniques like wound-wait and wait-die are used to handle situations where processes are waiting for resources, and a deadlock might occur. Let's break them down:

Wound-Wait:

Think of it like being a bit impatient. If a process needs a resource held by another process, it checks the age or "wound" of the other process.

If the other process is "younger" (started later), then the current process waits.

If the other process is "older" (started earlier), the current process can "wound" it, meaning it can force the older process to release its resources, preventing a deadlock.

Wait-Die:

This approach is more patient. If a process needs a resource held by another process, it checks the age or "age difference" between them.

If the other process is "younger," the current process waits.

If the other process is "older," the current process can "die," meaning it gives up and gets restarted, allowing the older process to continue and preventing a deadlock.

In both cases, the idea is to avoid deadlocks by carefully managing how processes interact. 

Factor analysis, Dimensionality reduction, Predictive analytics, Cluster Analysis, Decision Tree, Types of Decision Trees notes

Factor analysis: in factor analysis, latent variables are turned into factors i.e, they are reduced according to their functionality

Example waiting time, cleanliness, healthy and taste are latent variables

waiting time, cleanliness are service factors

healthy and taste are food experience factor

In a hotel or restaurant these latent variables are reduced to factors.

Descriptive analysis is very important in the sense of model building

Merits of factor analysis:
1. reduces amount of data
2. easy categorization
3. uses statistical methods
4. very important in model building
5. data is very easy to interpret as there are no outliers
6. is important descriptive analysis technique

Predictive analytics: is when we retrieve the unknown or future data with the help of past/current data
There are 4 types of predictive analysis
    1. classification
    2. Prediction
    3. Regression
    4. Time series
These types are used in different situations accordingly

 Dimensionality reduction: when there is data with high dimensionality working with it becomes difficult and tedious task.

to remove this we perform data reduction step in KDD process

Dimensionality reduction is a part of data reduction

Some dimensionality reduction:
1. Data cube: data is arranged in cube form to ensure there are less dimensions
2. Numerosity: we can apply rank to groups of data 
3. Feature selection: we need to find appropriate features using filtering, morphing etc
4. Normalization: to make all the data in same range or interval

Lazy Learning:  Lazy learning is a technique where we give a model some rules and testing criteria. But it only executes them when the data is given to them or from their neighbours

if we give data suppose a=2 and b=3 then only it learns from the formula otherwise it wont

it is useful when we have TB of data and we cant test all of it.

kNN is a lazy learning algorithm

Cluster Analysis
Cluster analysis is a kind of descriptive analysis where we group or cluster similar kind of data.
it is helpful for large amounts of unstructured data
it is used to further analysis and building models

Although there are different types of cluster analysis but the most popular ones are
1. partitioning clusters
2. hierarchical 
3. Density Based
4. Grid Based
1. Partitioning Methods: as the name suggest partitioning methods involves partitioning a data set and performing some analysis or methods or formulas to derive the cluster.
K- means and K- medoids are Partitioning methods

2. Hierarchical Methods: As the name suggests we need to form a hierarchy and then we can form one cluster. 
Agglomerative and Divisive are popular hierarchical methods

3. Density Methods: When we assign some density to the data points and combine them by their density. DBScan  is one of the popular density based clustering technique

4. Grid Based: in this method, we assign the density to the cluster and then store them in the form of a rectangular grid with cells
Then we apply decision tree and apply levels
Higher level cells are stored in one cluster and lower level in one

In cluster analysis, we need to choose the appropriate cluster technique for the datasets

Decision Tree: Here in this below example, 
weather is root node
humidity and speed are attributes/ columns
yes or no are class labels
This example shows if a child can play outside or not.

In decision tree, rectangle represents attributes/ columns
ellipse represents class labels

The main purpose of decision tree is to extract the rules for classification.

Example: if weather = sunny and humidity = normal then play = yes

if  weather = cloudy then play = yes

id weather = windy and speed = low then play = yes

Types of Decision Trees
1) un weighted decision tree: when there is no weight on any nodes of the decision tree, i.e, there are no biases in decision tree

2. weighted decision tree:

3. binary decision tree: where there are only two attributes or labels in a tree

4. Random forest: n number of decision trees combined