Python programming and uses
It us General purpose language
Used in occupational AI,ML,web apps, YouTube ..its syntax is easy to read, write but very slow to execute hence it is slow Language
Used in computers, networks, andSmall computers, large computers web apps, ML, glue language Paradigm
Python is ethical, nonethical, DS, ML, Al,scientific computers tasks easy to learn read write. Dynamic typing
Python uses instagram, YouTube, NASA, drop box,web scraping with python
Java script JS-1995,typescript
Both Java and JS are introducd in 1995
JS--Netscript, Web browsers just like python code run by engine
JS versatile, Animation, dialogues are the uses..
Java 3 million users
Mind crafe
Adobe
Android
C-section flexible and versatile allows low level codes, high level language
C fundamental concepts executes of low level concepts
C sharp (C#)
Window based application, mobile application unity create gaming
C++--oopL
Learn and write not for beginners
Graphical, faster, better options in Embedded systems Application programs Windows, MySQL, 3-D Gaming ,desktop, mobile applications.
Ruby
General purpose language similar to python, Object oriented applications
Ruby on rails
Web applications like Github , fiverr,airbnb
PHP-general purpose scripting language-- html code
Web development
Uses - facebook YouTube, Wikipedia, word press
Go introduced in 2012
Wide used programming
Robust system level language, currently used graphic servers like drop box--files, sourse folders..
Swift-programming language,iOS developer
R language.
SQL-data storage unit -DBMS
Java-andoid, S/W,desktop developments,big data,uses-paytm,airbnb developer ,uber, instagram, google
Programming technics of computer system... Software languages
Precedence and priority of arithmetic operaters...
prototypes -- intialization ,declaration, assigning of variables and consonants..
Printf for C,python-phycharm aap
System. Out. Print for C++, Java
Data science
Python
Tableau
Pandas
Data studio
Power BI
My SQL..
Kyros
AI (robotics, human made machines) and ML(computer with binary coding)
Data Analysis-techniques ,power of data
Data science
Python with Tensor flow .
Python with Deep learning..
Keras sequential, layers, loses, activation, optimizers, binarycross entropy, linearity and nonlinearity..Epoch, step loss, accuracy.. Neural network linear and nonlinear
Epoch, learning rate, activation(ReLu,linear,sigmoid,Tanh),regularization, regularization rate, problem type
Data, features, hidden layer, output study---Testloss, training loss
AI and ML..
--Statistics and exploratory Analytics
Data Analysis
Cloud essentials :Git and Github
Inferential statistics
Hypothesis testing
Lending club case study
Machine learning -1
Linear regression
Linear regression assignment
Naive Bayes
Model selection
Machine learning -2
Advanced regression
Advanced regression assignment
Support vector machine
Tree models
Model selection - practical considerations
Boosting
Unsupervised learning-clustering
Unsupervised learning - Principal analysis
Telecom churn case study
Deep learning
Introduction to Neural Networks
Convolutional Neural Networks-industrial Applications and assignment
Recurrent Neutral Networks
Neural network project - Gesture recognition..
Natural Language processing
Lexical processing
Syntactical processing
Syntactic processing assignment
Semantic processing
Case study : classifying customer complaint tickets
Deep learning with MLops
Cloud essentials AWS, working with AWS case study, MLOps,Fas to life cycle, model life cycle ,Deployment theory..
Capstone
News recommended system
Credit card fraud detection
Eye for blind. (image captioning)
Sentiment analysis product recommend system
Sales forecasting
Style transfer using GANS..
Reinforcement learning-micellanious reinforcement-- classical and deep reinforcement with project essentials for AI And ML professional..
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