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Full Stack PYTHON - Learnees

Full Stack PYTHON

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Dive deep into full-stack development with Python, specifically tailored for game development. This course builds upon your existing Python knowledge, equipping you with the skills to create interactive game experiences from front-end interfaces to robust back-end systems. Learn to leverage popular frameworks like Django and Flask for server-side logic, and explore front-end technologies like Pygame and advanced GUI libraries for captivating user interfaces, ultimately enabling you to build and deploy complete game applications.

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Course started on:

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1. Ai

Topics:

Introduction to AI with Python: Libraries and Ecosystem (NumPy
Pandas
Scikit-learn)
Supervised Learning: Regression and Classification Algorithms (Linear Regression
Logistic Regression
Decision Trees) using Scikit-learn
Model Evaluation and Selection: Metrics
Cross-Validation
and Hyperparameter Tuning
Unsupervised Learning: Clustering Techniques (K-Means
Hierarchical Clustering) and Dimensionality Reduction (PCA)
Natural Language Processing (NLP) Fundamentals: Text Preprocessing
Tokenization
and Sentiment Analysis with NLTK and SpaCy
Building and Deploying a Simple Machine Learning Web Application with Flask/Django
Introduction to Deep Learning with TensorFlow/Keras: Building a Basic Neural Network
Image Classification with Convolutional Neural Networks (CNNs)
Ethical Considerations and Bias in AI: Fairness
Accountability
and Transparency.

Hands-on:

Activity 1: **Basic Sentiment Analysis with NLTK:** (Individual Exercise) Students will use NLTK to perform sentiment analysis on a set of movie reviews or social media posts. This reinforces text processing and basic NLP concepts.
Activity 2: **Spam Email Detection Model:** (Individual Project) Students will build a machine learning model (e.g.
Naive Bayes or Logistic Regression) to classify emails as spam or not spam using a public dataset. This covers model training
evaluation
and feature engineering.
Activity 3: **Simple Chatbot with Rule-Based Responses:** (Individual Exercise) Students will create a basic chatbot that responds to user inputs based on predefined rules and keywords. This introduces chatbot development and rule-based AI.
Activity 4: **Image Classification with TensorFlow/Keras (MNIST Dataset):** (Individual Lab) Students will use TensorFlow/Keras to train a convolutional neural network (CNN) to classify handwritten digits from the MNIST dataset. This provides hands-on experience with deep learning and image classification.
Activity 5: **Predictive Modeling for Customer Churn:** (Collaborative Project) Teams will work on a real-world dataset to predict customer churn using machine learning algorithms. This emphasizes teamwork
data analysis
feature selection
and model comparison.
Activity 6: **Recommendation System for E-commerce:** (Collaborative Project) Teams will develop a simple recommendation system (e.g.
collaborative filtering or content-based filtering) for an e-commerce platform using a dataset of product ratings or purchase history. This introduces recommendation systems and collaborative filtering techniques.
Activity 7: **Facial Recognition Attendance System (Basic):** (Individual Assignment) Students will build a simple facial recognition attendance system that identifies and marks the attendance of students based on their facial features. This covers computer vision
face detection
and basic image processing.
Activity 8: **Deploying a Simple ML Model with Flask:** (Individual Assignment) Students will deploy one of their previous models (e.g.
spam detection or sentiment analysis) as a web service using Flask. This reinforces model deployment and API creation skills.

Skills You Will Learn:

Develop and train basic machine learning models using Python libraries (Scikit-learn)
Implement and utilize pre-trained AI models for image recognition and natural language processing
Integrate AI models into a Flask-based web application
Design and implement data pipelines for AI model training and deployment
Evaluate and interpret AI model performance metrics (accuracy
precision
recall)
Apply ethical considerations and bias detection techniques in AI development
Use APIs to access and integrate external AI services
Optimize AI model performance through hyperparameter tuning
Deploy AI models using cloud platforms (e.g.
AWS
Google Cloud)
Collaborate on AI-driven projects using version control (Git)

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What You'll Learn in Full Stack PYTHON

Master the essential skills and industry-leading tools that will prepare you for real-world challenges and career success

Python Fundamentals

Data Structures

HTML CSS Basics

RESTful APIs

Django Fundamentals

Databases and ORM

Flask Microservices

Deployment and DevOps

Curriculum Designed by Industry Experts

Master the skills employers are looking for with our comprehensive, hands-on curriculum

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1. Ai

Topics:

Introduction to AI with Python: Libraries and Ecosystem (NumPy
Pandas
Scikit-learn)
Supervised Learning: Regression and Classification Algorithms (Linear Regression
Logistic Regression
Decision Trees) using Scikit-learn
Model Evaluation and Selection: Metrics
Cross-Validation
and Hyperparameter Tuning
Unsupervised Learning: Clustering Techniques (K-Means
Hierarchical Clustering) and Dimensionality Reduction (PCA)
Natural Language Processing (NLP) Fundamentals: Text Preprocessing
Tokenization
and Sentiment Analysis with NLTK and SpaCy
Building and Deploying a Simple Machine Learning Web Application with Flask/Django
Introduction to Deep Learning with TensorFlow/Keras: Building a Basic Neural Network
Image Classification with Convolutional Neural Networks (CNNs)
Ethical Considerations and Bias in AI: Fairness
Accountability
and Transparency.

Hands-on:

Activity 1: **Basic Sentiment Analysis with NLTK:** (Individual Exercise) Students will use NLTK to perform sentiment analysis on a set of movie reviews or social media posts. This reinforces text processing and basic NLP concepts.
Activity 2: **Spam Email Detection Model:** (Individual Project) Students will build a machine learning model (e.g.
Naive Bayes or Logistic Regression) to classify emails as spam or not spam using a public dataset. This covers model training
evaluation
and feature engineering.
Activity 3: **Simple Chatbot with Rule-Based Responses:** (Individual Exercise) Students will create a basic chatbot that responds to user inputs based on predefined rules and keywords. This introduces chatbot development and rule-based AI.
Activity 4: **Image Classification with TensorFlow/Keras (MNIST Dataset):** (Individual Lab) Students will use TensorFlow/Keras to train a convolutional neural network (CNN) to classify handwritten digits from the MNIST dataset. This provides hands-on experience with deep learning and image classification.
Activity 5: **Predictive Modeling for Customer Churn:** (Collaborative Project) Teams will work on a real-world dataset to predict customer churn using machine learning algorithms. This emphasizes teamwork
data analysis
feature selection
and model comparison.
Activity 6: **Recommendation System for E-commerce:** (Collaborative Project) Teams will develop a simple recommendation system (e.g.
collaborative filtering or content-based filtering) for an e-commerce platform using a dataset of product ratings or purchase history. This introduces recommendation systems and collaborative filtering techniques.
Activity 7: **Facial Recognition Attendance System (Basic):** (Individual Assignment) Students will build a simple facial recognition attendance system that identifies and marks the attendance of students based on their facial features. This covers computer vision
face detection
and basic image processing.
Activity 8: **Deploying a Simple ML Model with Flask:** (Individual Assignment) Students will deploy one of their previous models (e.g.
spam detection or sentiment analysis) as a web service using Flask. This reinforces model deployment and API creation skills.

Skills You Will Learn:

Develop and train basic machine learning models using Python libraries (Scikit-learn)
Implement and utilize pre-trained AI models for image recognition and natural language processing
Integrate AI models into a Flask-based web application
Design and implement data pipelines for AI model training and deployment
Evaluate and interpret AI model performance metrics (accuracy
precision
recall)
Apply ethical considerations and bias detection techniques in AI development
Use APIs to access and integrate external AI services
Optimize AI model performance through hyperparameter tuning
Deploy AI models using cloud platforms (e.g.
AWS
Google Cloud)
Collaborate on AI-driven projects using version control (Git)

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Resolve Doubts in Real-time

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Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments

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Learnees Training Certificate
Graded Performance Certificate
Certificate of Completion

Full Stack PYTHON Certification

Master Skills & Earn Your Professional Certificate

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Why enroll for Full Stack PYTHON Certification?

Unlock your potential in with Full Stack PYTHON certification

Most Popular Certification

Full Stack PYTHON certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global Full Stack PYTHON market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an Full Stack PYTHON is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Course Overview: Full Stack PYTHON

Essential details: objectives, prerequisites, schedule, and outcomes

Develop and deploy a RESTful API using Flask or Django REST framework for game data management. Implement user authentication and authorization systems for secure game access and account management. Design and build interactive game front-ends using Pygame, Kivy, or similar GUI libraries, incorporating advanced UI/UX principles. Integrate back-end services with game front-ends to create a seamless and responsive user experience. Utilize relational databases (e.g., PostgreSQL, MySQL) to store and retrieve game data efficiently. Apply design patterns (e.g., MVC, Observer) to create maintainable and scalable game architectures. Implement real-time communication features (e.g., WebSockets) for multiplayer game functionality. Deploy a full-stack game application to a cloud platform (e.g., AWS, Heroku, Google Cloud). Write unit and integration tests to ensure the quality and reliability of game code. Optimize game performance for smooth gameplay and scalability.