Course Description
<p>Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.</p>
Curriculum
- Introduction to Programming
- Introduction to Python and How Python Works
- Introduction to Python Basic Course Structure
- How to install Python
- How to run Python Program using Command Line Window, IDLE, Notepad
- How to install Anaconda and run jupyter notebook for python
- Comments, Identifier, Reserved Words and Constant in Python
- Variable in Python
- Data Type in Python
- Declaring and Intializing Variables in Python
- Operators in Python
- Implicit and Explicit Type Conversion in Python
- Output Statement or Print Function in Python
- Getting input from user or Input Statement or input Function in Python
- Escape Sequence in Python
- If Statement in Python
- Nested if Statement in Python
- if Statement with Logical Operator in Python
- Indentation in Python
- if else Statement in Python
- Nested if else Statement in Python
- if elif and if elif else Statement in Python
- range Function in Python
- For Loop and Nested for Loop in Python
- Enumerate() in Python with EXAMPLES
- While Loop and Nested while Loop in Python
- Break, Continue and Pass Statement in Python
- String in Python
- More on Strings(Access string using loop, repetitation and concatenation in string, mutable and immutable)
- String Formatting in Python
- String Methods in Python
- List in Python
- List methods in python
- More on Lists (Use loops with list, slicing in list, concatenation, repeatition, aliasing, copying and cloning in list)
- Nested List in Python
- Tuple and tuple methods in Python
- More on Tuple(Use loops with tuple, slicing tuple, concatenation, repeatition, aliasing, copying tuple, getting user input as tuple, modifying and deleting tuple)
- Nested Tuple in Python
- Set Type in Python
- Set methods in Python
- Nested Set in Python - FrozenSet
- Dictionary in Python
- Dictionary methods in Python
- More on Dictionary (Loop in Dictionary, Getting Dictionary input from user)
- Nested Dictionary in Python
- Function and How Function Work in Python
- Arguments and its types in Python
- Local and Global Variable in Python
- global Keyword and function in Python
- Passing and returning List, Tuple, Set, Dictionary in Function
- Recursion in Python
- Anonymous Function or Lambda Expression in Python
- Function Decorator in Python
- Ternary if
- List, Tuple, Set and Dictionary Comprehension in Python
- Nested Comprehension in Python
- Generator Function Yield Statement and Next Function in Python
- Type and isinstance Function in Python
- Len,min,max and sorted Function
- Zip method
- Filter, map Function in Python
- Tips and Tricks in Python
- Getting Help in Python
- Class and Object in Python
- Constructor in Python
- Inheritance in Python
- Constructor in Inheritance
- Method Overloading, Overriding and Method with super in Python
- Special (Magic/Dunder) Methods
- Exception Handling and Builtin Exception in Python
- User Defined Exception in Python
- Difference between Error and Exception and Warning in Python
- Installing VSCode and coding in VSCode
- Debugging in VSCode
- Introduction to Virtual Environments
- Using conda and virtual environments with VSCode
- Module in Python, Math module in python
- Datetime Class in Python
- Random, sleep in Python
- Functools(Reduce and Memoization), getpass(getpass and getuser) and sys Module
- if __name__ == "__main__"
- What is File and File Handling in Python
- Reading Files in Python
- Write Create Files in Python
- What is git/ What is Version control system?
- What is Github?
- How To Insatll Git
- Basic Commands: add, commit, push
- Undoing/Reverting/Resetting code changes
- Branches (Create, Merge, Delete)
- What is HEAD?
- .gitignore file
- Diff and Merge using vscode
- What is Pull Request?
- Brief intro to working with git, github with help of vscode and github desktop
- Introduction to Libraries
- Introduction to Web Scrapping - Beautiful Soup
- Project 1 - Web scraping table from wikipedia
- PyQt Tutorial: Python GUI Designer
- Project 2 - Making a simple calculator
- Image Manipulation with Pillow
- Project 3 - Adding logo into Multiple images at once.
- Data on the Web
- eXtensible Markup Language(XML)
- XML Schema
- Parsing XML
- JSON Introduction
- Encoding JSON Python Objects
- Decoding JSON Python Objects
- Connecting to a server
- A simple server-client program
- What is Database?
- Connect to Database in Python
- Create a Table
- Insert Record into Table
- Query and Fetchall
- Use the Where Clause
- Introduction to Machine Learning
- Key ML Terminology
- Linear Regression
- Training and Loss
- An alternative Approach
- Gradient Descent
- Learning Rate
- Optimizing Learning Rate
- Stochastic Gradient Descent
- Toolkit
- Peril of Overfitting
- Splitting Datat
- Another Partition
- Feature Engineering
- Qualities of good feature
- Cleaning Data
- Encoding Nonlinearity
- Crossing One Hot Vectors
- L2 Regularization
- Lambda
- L2 Regularization Exercise
- Calculating a Probability
- Loss and Regularization
- Thresholding
- True vs False, Positive vs Negative
- Accuracy
- Precision and Recall
- ROC Curve and AOC
- Prediction Bias
- L1 Regularization
- Structure
- Best practices
- One vs All
- Softmax
- Motivation from collaborative filtering
- Categorical Input Data
- Translating to a Lower Dimensional Space
- Obtaining Embeddings
- Production ML S
- Static vs Dynamic Training
- Static vs Dynamic Interface
- Data Dependencies
- Fairness
- Types of Bias
- Identifying Bias
- Evaluating for Bias
- Cancer Prediction
- Introduction to Google co-lab and Anaconda
- Numpy
- Pandas
- Plotting and Charting
- Project1: Mini Project
- Tensorflow
- Pytorch
- Keras
- Theano
- Computer Vision Overview
- Image Formation
- History
- Introduction to Open CV
- Different Types of Filter
- Feature Detection
- Edge Detection
- Haar-like Features
- Frequency Domain Analysis
- Project2: Face Detection
- Introduction to Deep Learning
- History
- Why Deep Learning Taking Off
- Building Block of deep Learning
- Application of Deep Learning
- Multilayer Perceptron
- Back Propagation
- Working Of neural network
- Adjusting the weights
- Gradient Descent
- Stochastic Gradient Descent
- Foundations of Convolutional Neural Network
- CNN Architecture
- Convolution Operation
- ReLU Layer
- Pooling
- Flattening
- Full Connection
- Softmax & Cross-Entropy
- Summary
- Get the dataset
- Importing the Libraries
- Importing the Dataset
- Splitting the Dataset into the Training set and Test set
- Feature Engineering
- Traditional Computer Vision
- Image classification using Deep Learning
- Binary and Multi class Image classification
- Deep learning in Object Detection SSD,YOLO
- Project3: Binary and Multi Image Classification
- Project4: Object Recognition
- The Idea Behind GANs
- How Do GANs Work?
- Generator
- Discriminator
- Generative Adversarial Networks Representation
- Mathematical Details About GANs
- Applications of GANs
- Current Research On GAN
- Project 5: Image Creation with GAN
