Software Development

Data Science and AI-ML Foundations and Building Applications with Flask and Django

Learn data science, AI/ML basics, and build real-world applications with Flask/Django.

Data Science and AI-ML Foundations and Building Applications with Flask and Django
Track Software Development
Duration 60 hours
Skill Level Intermediate
Language English

About this Course

Learn data science, AI/ML basics, and build real-world applications with Flask/Django.
Learning Mode: Learn at ALC or at Home

Detailed Course Curriculum

Hands-on module breakdown aligned with MKCL production standards and industry requirements.

  • Introduction
  • 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
  • Intro video
  • Numpy
  • Pandas
  • Matpotlib
  • SciKit-Learn
  • Tensorflow
  • Keras
  • PyTorche
  • The Natural Langauge Toolkit
  • XGBoost
  • CatBoost
  • OpenCV
  • Introduction of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Data Preprocessing
  • Feature Extraction
  • Training data
  • Which model to use?
  • Overfitting / Underfitting
  • The necessity of Statistics for AI
  • Vectors and Matrices
  • Graphs for AIML
  • Sets for AIML
  • Probability distribution
  • Hypothesis testing in AIML
  • Markov model
  • Clustering in AIML
  • Kernal Functions in AIML
  • 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
  • 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
  • What are Neural Networks
  • History
  • Types of Neural Network
  • Weights and Biases
  • How do Neural Networks Work?
  • Working of some common Neural Networks
  • Neural Network vs Deep Learning
  • Applications of Neural Network
  • 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)-
  • 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
  • Introduction To Data Science
  • What is Data Science
  • What is Data Science
  • Who is Data Scientist?
  • Who is Data Scientist?
  • Why Data Science
  • Why Data Science
  • Data Science Pipeline
  • Data Science Pipeline
  • Data Science Tools
  • Data Science Tools
  • Data Science Tools (Proprietary)
  • Data Science Tools (Proprietary)
  • Introduction to Python Tools for Data Science
  • Introduction to Python Tools for Data Science
  • Anaconda Installation and Setup
  • Anaconda Installation and Setup
  • Virtual Environment Setup with Anaconda
  • Virtual Environment Setup with Anaconda
  • What is PYPI?
  • What is PYPI?
  • Installing Packages via Pip
  • Installing Packages via Pip
  • Jupyter Notebook
  • Jupyter Notebook
  • What is a Jupyter Notebook
  • What is a Jupyter Notebook
  • What is a Jupyter Notebook
  • Getting familiar with Jupyter Notebook
  • Getting familiar with Jupyter Notebook
  • Getting familiar with Jupyter Notebook
  • Jupyter Magic Commands
  • Jupyter Magic Commands
  • Jupyter Magic Commands
  • Case Studies
  • Case Studies
  • Covid 19 Data Science Application
  • Covid 19 Data Science Application
  • Covid 19 Data Science Application
  • JP Morgan
  • JP Morgan
  • JP Morgan
  • Netflix User Case
  • Netflix User Case
  • Netflix User Case
  • UPS
  • UPS
  • UPS
  • Walmart
  • Walmart
  • Walmart
  • Future of Data Scientist
  • Future of Data Scientist
  • Maths
  • Vector Introduction
  • Vector Introduction
  • Vector Arithmetic
  • Vector Arithmetic
  • Dot and cross product
  • Dot and cross product
  • Applications of Vectors
  • Applications of Vectors
  • Probability Introduction
  • Probability Introduction
  • Conditional probability
  • Conditional probability
  • Multiplication Rule of probability
  • Multiplication Rule of probability
  • Baye’s Theorem
  • Baye’s Theorem
  • Statistics Introduction
  • Statistics Introduction
  • Discrete and continuous mathematics
  • Discrete and continuous mathematics
  • Set Theory
  • Set Theory
  • Applications of set theory
  • Applications of set theory
  • Relations and Functions
  • Relations and Functions
  • Numpy
  • Introduction to numpys
  • Introduction to numpys
  • Creating numpy arrays and dimensions
  • Creating numpy arrays and dimensions
  • Indexing
  • Indexing
  • Numpy Slicing
  • Numpy Slicing
  • Numpy Arithmetic Operations
  • Numpy Arithmetic Operations
  • Other Numpy Arithmetic Operations
  • Other Numpy Arithmetic Operations
  • Broadcasting and comparison
  • Broadcasting and comparison
  • Solving equation with numpy
  • Solving equation with numpy
  • Statistical Operation with numpy
  • Statistical Operation with numpy
  • Create and manipulate arrays using numpy
  • Create and manipulate arrays using numpy
  • Combining 2 arrays
  • Combining 2 arrays
  • Compare the elements of the two arrays
  • Compare the elements of the two arrays
  • Program to print 2d diagonal array.
  • Program to print 2d diagonal array.
  • Flattening a 2d array
  • Flattening a 2d array
  • Python program explaining numpy.size () function
  • Python program explaining numpy.size () function
  • Non-Zero Functions with numpy
  • Non-Zero Functions with numpy
  • Changing Data Type
  • Changing Data Type
  • Trace of matrix
  • Trace of matrix
  • Addition of two matrix
  • Addition of two matrix
  • Subtraction of Two Matrix
  • Subtraction of Two Matrix
  • Tabular analysis with Pandas
  • Introduction to pandas
  • Introduction to pandas
  • Data structures in pandas
  • Data structures in pandas
  • Reading files in Csv
  • Reading files in Csv
  • Retrieving data
  • Retrieving data
  • Analysing data
  • Analysing data
  • Querying and sorting
  • Querying and sorting
  • Working with dates
  • Working with dates
  • Grouping and aggregation
  • Grouping and aggregation
  • Merging data from multiple sources
  • Merging data from multiple sources
  • Writing data back to files
  • Writing data back to files
  • Basic Plotting with Pandas
  • Basic Plotting with Pandas
  • How to create a DataFrame in Pandas from a dictionary of arrays/lists
  • How to create a DataFrame in Pandas from a dictionary of arrays/lists
  • Creating Dataframe from lists
  • Creating Dataframe from lists
  • Creating Dataframe from a list of tuples
  • Creating Dataframe from a list of tuples
  • Create a list of nested dictionaries
  • Create a list of nested dictionaries
  • Pandas to create a dataframe
  • Pandas to create a dataframe
  • Displays the values of each row and column using pandas
  • 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 read data from a string using the pandas read_csv() function
  • How to reindex the rows of a Pandas DataFrame using the reindex() method
  • How to reindex the rows of a Pandas DataFrame using the reindex() method
  • Create two pandas Series using the NumPy linspace() function
  • Create two pandas Series using the NumPy linspace() function
  • Preprocess Data and Matplotlib
  • Preprocess Data
  • Preprocess Data
  • Why preprocess
  • Why preprocess
  • Why preprocess
  • Preprocessing Technique
  • Preprocessing Technique
  • Preprocessing Technique
  • Null and NaN
  • Null and NaN
  • Null and NaN
  • Forward Fill
  • Forward Fill
  • Forward Fill
  • Selecting data with conditionals
  • Selecting data with conditionals
  • Selecting data with conditionals
  • Dropping columns/rows
  • Dropping columns/rows
  • Dropping columns/rows
  • Subset and index data
  • Subset and index data
  • Subset and index data
  • Reshaping
  • Reshaping
  • Reshaping
  • Pivoting
  • Pivoting
  • Pivoting
  • Rank and sort data
  • Rank and sort data
  • Rank and sort data
  • Matplotlib
  • Matplotlib
  • Introduction to Matplotlib
  • Introduction to Matplotlib
  • Introduction to Matplotlib
  • Linchart
  • Linchart
  • Linchart
  • Improving style using seaborn
  • Improving style using seaborn
  • Improving style using seaborn
  • Scatter plot
  • Scatter plot
  • Scatter plot
  • Histogram
  • Histogram
  • Histogram
  • BarChart
  • BarChart
  • BarChart
  • HeatMap
  • HeatMap
  • HeatMap
  • Exploratory Data Analysis
  • EDA Introduction
  • EDA Introduction
  • Data Preparation and Cleaning
  • Data Preparation and Cleaning
  • Exploratory Analysis
  • Exploratory Analysis
  • Asking and answering the questions Zale
  • Asking and answering the questions Zale
  • What is 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
  • Basic Flask Concepts
  • Server Startup and A complete Application
  • Templates- Part 1
  • Templates- Part 2
  • Templates- Part 2
  • Flask- Bootstrap
  • Static Files
  • Flask HTTP methods
  • Request Response Cycle- Part 1
  • Request Response Cycle- Part 2
  • Request Response Cycle- Part 2
  • Error Handling and debugging- Part 1
  • Error Handling and debugging- Part 2
  • Error Handling and debugging- Part 2
  • Python Database Framework
  • SQLAlchemy ORM- Part 1
  • SQLAlchemy ORM- Part 2
  • SQLAlchemy ORM- Part 2
  • CRUD Operations
  • Creating tables in flask
  • Creating tables in flask
  • Inserting into table
  • Inserting into table
  • Querying Data
  • Querying Data
  • Updating Data
  • Updating Data
  • Deleting Data
  • Deleting Data
  • Database Migration
  • Web Forms
  • Email Support
  • Flask Blueprint
  • Template handling
  • Template handling
  • Static File handling
  • Static File handling
  • Error handling
  • Error handling
  • Flask Blueprint Decorator
  • Flask Blueprint Decorator
  • Abort function
  • Abort function
  • Flask Application Structure
  • Configuration Options
  • Configuration Options
  • Application package
  • Application package
  • Application factory
  • Application factory
  • Implementing Application functionality in Blueprint
  • Implementing Application functionality in Blueprint
  • Introduction to API
  • Web API
  • Web API
  • REST Framework
  • REST Framework
  • Postman Tool
  • Working with API Part 1
  • Working with API Part 2
  • Working with API Part 2
  • Working with API Part 3
  • Working with API Part 3
  • Designing RESTful APIs- Part 1
  • Designing RESTful APIs- Part 2
  • Designing RESTful APIs- Part 2
  • Common Flask Extensions
  • Deployment Workflow
  • 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
  • manage.py
  • init.py
  • init.py
  • settings.py
  • settings.py
  • urls.py
  • urls.py
  • wsgi .py
  • wsgi .py
  • asgi.py
  • asgi.py
  • app in Django?
  • admin.py
  • admin.py
  • app.py
  • app.py
  • models.py
  • models.py
  • test.py
  • test.py
  • views.py
  • views.py
  • Difference between project and app
  • Creating a project and an app
  • Registering an app
  • Runserver
  • Creating a View
  • Mapping the view to ulrs
  • 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
  • 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
  • Django Views and its types
  • Function Based View
  • CRUD Operations Using FBV
  • CRUD Operations Using FBV
  • Insert Operation
  • Insert Operation
  • Update Operation
  • Update Operation
  • Delete Operation
  • Delete Operation
  • Class-based views (CBVs)- Part I
  • Class-based views (CBVs)- Part II
  • Class-based views (CBVs)- Part II
  • CRUD Operations Using CBV- Part I
  • CRUD Operations Using CBV- Part II
  • CRUD Operations Using CBV- Part II
  • CRUD Operations Using CBV- Part III
  • CRUD Operations Using CBV- Part III
  • Mixins in Django
  • URLs import reverse
  • 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
  • Inbuilt middlewares
  • Middleware structure
  • Configuration of multiple middleware classes- Example 1
  • Configuration of multiple middleware classes- Example 2
  • Configuration of multiple middleware classes- Example 2
  • 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
  • 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
  • SOAP Based Web Services
  • REST (Representational State Transfer) web services
  • REST (Representational State Transfer) web services
  • Difference between soap and restful webservices
  • Difference between soap and restful webservices
  • Django Rest Framework Installation
  • 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
  • Views and its type in DRF
  • APIView
  • Creating a Simple API
  • Creating a Simple API
  • GenericAPIView
  • Creating a GenericAPIView
  • Creating a GenericAPIView
  • ViewSets
  • ViewSet actions
  • Custom ViewSet base classes
  • Defining Router for TestViewSet
  • Difference between APIView and ViewSet
  • 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
Eligibility Criteria
• Basic knowledge of computers and keen desire to build skills in this field.
• Open to students, job seekers, and working professionals.
Official Certification
• Official MKCL KLiC Certificate upon successful completion of the course and evaluations.
Work-Centric Learning Approach
• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!

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Course Overview
  • Duration: 60 hours
  • Track: Software Development
  • Skill Level: Intermediate
  • Language: English
  • Mode: Learn at ALC or at Home
  • Certificate: Official MKCL
Key Course Highlights:
  • Understand AI and Machine Learning Basics: Apply basic concepts of AI and machine learning to Python projects.
  • Conceptual Mastery: Demonstrate a clear understanding of AI, ML, and Data Science principles and their interrelationships.
  • Technical Expertise: Proficient in using Python libraries and tools for building and evaluating machine learning models and conducting data analysis.
  • Model Implementation: Able to design, develop, and implement AI/ML models for classification, regression, and clustering tasks.
  • Data Handling and Visualization: Effectively preprocess data, perform detailed analysis, and visualize insights using libraries like Pandas and Matplotlib.
  • Ethical Awareness and Application: Students will identify ethical concerns in AI/ML and demonstrate the ability to apply AI/ML concepts in real-world scenarios responsibly.
  • Build web apps proficiently in both Flask and Django frameworks.
  • Set up the development environments while ensuring smooth coding experiences in Flask and Django.
  • Develop a solid foundation in HTML and CSS, enabling the creation of visually appealing web pages.
  • Discover the web development in Flask web development, including template handling and static file management.
  • Build skills in Flask database development, understanding SQLAlchemy ORM and CRUD operations.
  • Prepare RESTful APIs in Flask, demonstrating knowledge of application structure and configuration options.
  • Utilizing common Flask extensions and understanding the deployment workflow.
  • Build a solid understanding of Django, including project and app creation, views, and templates.
  • Build Django web applications with CRUD functionality, utilizing Django template language and static file management.
  • Designing data management in Django, understanding models, ORM, query sets, and forms for effective database interactions.
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