Introduction to Flask

• What is a Flask?
• History of Flask
• Why use Flask?
• Flask vs Django
• Setting up a development environment
• Installing Flask and Hello World
• Introduction to html and CSS
• HTML syntax and Structure
• Working With Classes and IDs
• CSS basics and syntax
• Creating a basic html document
• Styling Webpage with CSS

Flask Web Development

• Basic Flask Concepts
• Server Startup and A complete Application
• Templates
• Flask- Bootstrap
• Static Files
• Flask HTTP methods
• Request Response Cycle
• Error Handling and debugging

Flask Database Development and Web Application Tools

• Python Database Framework
• SQLAlchemy ORM
• CRUD Operations
• Creating tables in flask
• Inserting into table
• Querying Data
• Updating Data
• Deleting Data
• Database Migration
• Web Forms
• Email Support
• Flask Blueprint
• Template handling
• Static File handling
• Error handling
• Flask Blueprint Decorator
• Abort function

Flask RESTful APIs

• Flask Application Structure
• Configuration Options
• Application package
• Application factory
• Implementing Application functionality in Blueprint
• Introduction to API
• Web API
• REST Framework
• Postman Tool
• Working with API
• Designing RESTful APIs
• Common Flask Extensions
• Deployment Workflow

Demystifying Django

• What is Django?
• Django history
• Features of Django
• Disadvantages of Django
• Applications of Django
• Django MVC-MVT architecture
• Why Use Django?
• Webserver
• Virtual Environment
• Django Installation
• Project in Django
• manage.py
• init.py
• settings.py
• urls.py
• wsgi .py
• asgi.py
• App in Django
• admin.py
• app.py
• models.py
• test.py
• views.py
• Difference between project and app

Building a Django Web Application

• Creating a project and an app
• Registering an app
• Runserver
• Creating a View
• Mapping the view to URLs
• Creating a models
• Registering a model
• Make migrations
• Migrate
• Creating a superuser
• Settings.py
• Middleware
• Django database connection
• Small Project on Django
• Templates
• Django Template Language (DTL)
• Creating a template directory
• Rendering a template
• Template Variables
• Template Tag
• Template Filters
• If-else and For loop
• Static Files

Data Management in Django (Models and Forms)

• Models in Django
• Django model Data Types and Fields list
• ORM’s in Django
• Query Sets in Django
• Model instances
• Django forms
• Django Model Forms
• Form widgets
• CSRF Token
• Django formsets and Modelform sets

Exploring Django Views

• Django Views and its types
• Function Based View
• CRUD Operations Using FBV
• Insert Operation
• Update Operation
• Delete Operation
• Class-based views (CBVs)
• CRUD Operations Using CBV
• Mixins in Django
• URLs import reverse

Django Web Development: Sessions, Cookies, Serialization, Deserialization, Mixins, Authentication, and Authorization

• Session and Cookies in Django
• Enabling and configurating session
• Session serialisation
• working of cookies in Django
• Use of cookies
• Managing Cookies in Django: Setting, Modifying, Updating, and Deleting
• Enabling and Disabling Cookies in Django
• Django Serialization and Deserialization
• Model serialization
• Model inheritance styles
• Authentication
• default authentication implementations
• Working with the user object
• Authentication with web request
• Authorization
• Permission caching and default Permission
• Authentication vs Authorizations

Django Middleware

• Django Middleware
• Inbuilt Middlewares
• Middleware structure
• Configuration of multiple middleware classes
• Maintenance Mode Application
• Middleware application to show meaningful response
• Advantages and Disadvantages of Middlewares
• Mail system in Django
• Other Email Functions
• CSV Using Models
• PDF Using Models

Django Rest Framework

• Introduction to Django Rest Framework
• Django Rest framework History and Versions
• Advantages and Disadvantages of Django Rest Framework
• Applications of Django Rest Framework
• Web API VS Web Browser
• Webservices and its types
• SOAP Based Web Services
• REST (Representational State Transfer) web services
• Difference between soap and restful webservices
• Django Rest Framework Installation

Django Rest Framework Serializer and Deserializer

• Serializers in DRF
• Model Serializers in DRF
• Advantages and disadvantages of serializers in DRF
• Serializer mixins
• Serializer Fields
• Serializer Relations
• Deserializers in DRF
• JSON in DRF
• Serializers and Deserializers in CRUD Operations
• Advantages and disadvantages of deserializer
• Validation of serializer
• Difference serializer and deserializer in DRF

Django Rest Framework Views

• Views and its type in DRF
• APIView
• Creating a Simple API
• GenericAPIView
• Creating a GenericAPIView
• ViewSets
• ViewSet actions
• Custom ViewSet base classes
• Defining Router for TestViewSet
• Difference between APIView and ViewSet

DRF Authentication, Authorization and Pagination

• Authentication and Setting Authentication Schemes in DRF
• Token based authentication in DRF
• BasicAuthentication
• SessionAuthentication
• Custom authentication
• Difference between TokenBased, Basic, Session and Custom Authentication
• Unauthorized and Forbidden responses
• Oauth
• Authorization and permissions
• Pagination in Django Rest Framework
• Use of pagination
• Implementing pagination in an API view
• HTML pagination controls
• Filters in DRF

Introduction To Data Science

• What is Data Science?
• Who is Data Scientist?
• Why Data Science
• Data Science Pipeline
• Data Science Tools
• Data Science Tools (Proprietary)
• Introduction to Python Tools for Data Science
• Anaconda Installation and Setup
• Virtual Environment Setup with Anaconda
• What is PYPI?
• Installing Packages via Pip
• Jupyter Notebook
• What is a Jupyter Notebook
• Getting familiar with Jupyter Notebook
• Jupyter Magic Commands
• Case Studies
• Covid 19 Data Science Application
• JP Morgan
• Netflix User Case
• UPS
• Walmart
• Future of Data Scientist
• Maths
• Vector Introduction
• Vector Arithmetic
• Dot and cross product
• Applications of Vectors
• Probability Introduction
• Conditional probability
• Multiplication Rule of probability
• Baye’s Theorem
• Statistics Introduction
• Discrete and continuous mathematics
• Set Theory
• Applications of set theory
• Relations and Functions

Numpy

• Introduction to Numpys
• Creating numpy arrays and dimensions
• Indexing
• Numpy Slicing
• Numpy Arithmetic Operations
• Other Numpy Arithmetic Operations
• Broadcasting and comparison
• Solving equation with numpy
• Statistical Operation with numpy
• Numpy
• Create and manipulate arrays using numpy
• Combining 2 arrays
• Compare the elements of the two arrays
• Program to print 2d diagonal array.
• Flattening a 2d array
• Python program explaining numpy.size () function
• Non-Zero Functions with numpy
• Changing Data Type
• Trace of matrix
• Addition of two matrix
• Subtraction of Two Matrix

Tabular analysis with Pandas

• Introduction to pandas
• Data structures in pandas
• Reading files in Csv
• Retrieving data
• Analysing data
• Querying and sorting
• Working with dates
• Grouping and aggregation
• Merging data from multiple sources
• Writing data back to files
• Basic Plotting with Pandas
• Pandas Exercise
• How to create a DataFrame in Pandas from a dictionary of arrays/lists
• Creating Dataframe from lists
• Creating Dataframe from a list of tuples
• Create a list of nested dictionaries
• Pandas to create a dataframe
• Displays the values of each row and column using pandas
• How to read data from a string using the pandas read_csv() function
• How to reindex the rows of a Pandas DataFrame using the reindex() method
• Create two pandas Series using the NumPy linspace() function

Preprocess Data and Matplotlib

• Why preprocess?
• Preprocessing Technique
• Null and NaN
• Forward Fill
• Selecting data with conditionals
• Dropping columns/rows
• Subset and index data
• Reshaping
• Pivoting
• Rank and sort data
• Matplotlib
• Introduction to Matplotlib
• LineChart
• Improving style using seaborn
• Scatter plot
• Histogram
• BarChart
• HeatMap

Exploratory Data Analysis

• EDA Introduction
• Data Preparation and Cleaning
• Exploratory Analysis
• Asking and answering the questions

Introduction to Artificial Intelligence

• What is Artificial Intelligence?
• The history of AI and its Development
• Narrow or Weak AI
• AI Techniques and Algorithms
• Natural Language Processing
• The Ethical and Societal Implications of AI
• Relationship between AI and other Technologies
• Robotics and its connection to AI
• Difference between AI and ML
• The Role of AI
• Applications of AI
• Use of AI in Social Media

Introduction to Python libraries used for AI/ML

• Numpy
• Pandas
• Matpotlib
• SciKit-Learn
• Tensorflow
• Keras
• PyTorche
• The Natural Language Toolkit
• XGBoost
• CatBoost
• OpenCV

Introduction to Machine Learning and Mathematical/Statistical Concepts for AI/ML

• Introduction of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Data Preprocessing
• Feature Extraction
• Training data
• Which model to use?
• Overfitting / Underfitting
• Mathematical/Statistical Concepts for AI/ML
• The necessity of Statistics for AI
• Vectors and Matrices
• Graphs for AI-ML
• Sets for AI-ML
• Probability distribution
• Hypothesis testing in AI-ML
• Markov model
• Clustering in AI-ML
• Kernal Functions in AI-ML
• Decision Tree

Supervised Learning

• Introduction to Supervised Learning
• Classification
• Regression
• Naive Bayes
• Linear Regression
• Logistic Regression
• Support Vector Machines (SVMs)
• K Nearest Neighbor
• Supervised Learning Applications
• Challenges in Supervised Learning

Unsupervised Learning

• Introduction to Unsupervised Learning
• Clustering in Unsupervised Learning
• Exclusive and Overlapping Clustering
• Hierarchical Clustering
• Probabilistic Clustering
• Association Rule
• Dimensionality Reduction in Unsupervised Learning
• Principal Component Analysis
• Applications of Unsupervised Learning
• Challenges in Unsupervised Learning

Basics of Neural Network

• What are Neural Networks?
• History
• Types of Neural Network
• Weights and Biases
• How do Neural Networks Work?
• Working of common Neural Networks
• Neural Network vs Deep Learning
• Applications of Neural Network

Building a ML Model using Python

• Define the problem and determine the goals of the model
• Data preparations
• Factors to consider while choosing model
• Why to use CSV file?
• Building the ML model (Logistic Regression)

Evaluating an AI/ML Model

• Importance to evaluate the ML model
• Accuracy of ML model
• Precision measure of the ML model
• Recall
• F1 Score
• Confusion Matrix
• Techniques to improve accuracy