- Introduction to Git
- Version Control
- Git Commands
- Collaboration with Git
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Advanced Git Usage
- Git Hooks
- Git Submodules
đź“…Start Date:
Jun 15, 2025
🏷️Course Fees:
â‚ą15,000
3 Months of Intensive Training
Build 1 Live Projects
Learn from experienced professionals
Resume & Portfolio Building
Interview Preparation
Mock Interviews
Job Assistance
Reserve Your Seat Today!
About AI Development
AI Development involves creating intelligent systems and applications that can simulate human behavior, learn from data, and make decisions. It covers areas like machine learning, deep learning, natural language processing, and computer vision. AI developers use a range of tools and frameworks to build smart, efficient, and scalable solutions across various domains like healthcare, finance, robotics, and more.
- Front-end: Matplotlib, HuggingFace, Gradio, DASH
- Python: Python Fundamentals, Advanced Python
- Artificial Intelligence and Data Science: Scientific Computing, Machine Learning, Data Processing, Deep Learning, ML Deployment
- AI Frameworks: Scikit-Learn, PyTorch, TensorFlow, HuggingFace, Gradio
- Tools: Jupyter Notebook, Google Colab
Course Objectives
The AI Developer Training program is structured to provide learners with a solid foundation in Artificial Intelligence, empowering them to build intelligent systems and real-world AI solutions. The primary objectives of this course include:
- Introduction to AI & Machine Learning: Gain a comprehensive understanding of Artificial Intelligence concepts, types of machine learning, and their real-world applications across industries like healthcare, finance, and automation.
- Frontend Technologies: Learn Matplotlib,HuggingFace, Gradio and DASH to create user-friendly interfaces for AI-powered web applications.
- Backend Technologies: Build robust backend systems using core Python skills, including Python Fundamentals and Advanced Python, while integrating Scientific Computing, Machine Learning, Data Processing, and Deep Learning workflows. Leverage these capabilities to ensure seamless interaction between AI models and user interfaces, with smooth end-to-end ML deployment.
- End-to-End AI Application Development: Acquire the skills to design and implement complete AI applications—integrating data ingestion, model training, backend logic, and frontend interaction.
- Version Control: Learn to use Git and GitHub for managing code changes, collaborating on projects, and maintaining a structured development workflow.
- Deployment and Hosting: Explore deployment of AI models and applications using services like Heroku, AWS, or Google Cloud, ensuring scalability and performance in production.
- Security Considerations: Understand the importance of data privacy, model integrity, and ethical AI practices to secure and responsibly deploy AI applications.
- Testing and Debugging: Gain experience in testing machine learning models and debugging application code to ensure accuracy, performance, and reliability.
- Project Development: Work on real-world AI projects and practical exercises, allowing learners to apply concepts in scenarios such as image classification, chatbot development, or predictive analytics.
Prerequisites
To get the most out of the AI Developer Training program, learners are expected to have the following foundational knowledge and skills:
- Basic understanding of programming concepts – such as variables, loops, and functions.
- Knowledge of object-oriented programming (OOP) principles – including classes, inheritance, and encapsulation.
- Understanding of web development fundamentals – such as HTML, CSS, and basic JavaScript.
Training Content
The AI Developer Training Program provides a structured path starting from Python fundamentals such as data types, control flow, functions, and collections. It then moves into advanced topics like exception handling, file operations, and GUI development with Tkinter. Learners explore scientific computing using NumPy and Pandas for data analysis and visualization. The program introduces machine learning with Scikit-Learn, covering data preparation, classification, model training, and evaluation. Data visualization is enhanced through Matplotlib, while SciPy introduces advanced mathematical operations. The course emphasizes essential data processing techniques like cleaning, scaling, encoding, and outlier handling. It also covers deep learning with PyTorch and TensorFlow, where learners build and train neural networks. Finally, the training includes deploying models using HuggingFace and Gradio, enabling participants to build interactive AI applications and host them effectively.
Git
Agile
- Agile Fundamental
- Agile Manifesto & Principles
- Agile vs. Waterfall
- Agile Frameworks & Workflows
- Scrum Framework (Roles, Events, Artifacts, Process Flow)
- Scrum Tools, Estimation & Scaling
Python Fundamentals
- Basic Data Types
- Working with Assignment Operators
- Operator Overloading
- Control Flow Structures
- Advanced Iteration Techniques
- Functions in Python
- Arrays in Python
- Strings in Detail
- List Operations
- Tuple Operations
- Dictionary Operations
Advanced Python
- Exception Handling
- File Handling
- Unit Testing
- Debugging Techniques
- GUI Development with Tkinter
Data Analysis with Python
- Introduction to NumPy
- Array Operations and Manipulation
- Indexing, Slicing, and Shape Handling
- NumPy Functions and Methods
- Introduction to Pandas
- Working with Series and DataFrames
- Data Indexing and Selection
- Data Cleaning and Preprocessing
- Data Analysis and Manipulation
- Data Visualization with Pandas
Introduction to Machine Learning with Scikit-Learn
- Overview of Scikit-Learn
- Dataset Loading and Preparation
- Classification Algorithms
- Splitting Data into Training, Test, and Validation Sets
- Learning and Prediction
- Performance Analysis and Evaluation
Data Visualization with Matplotlib
- Introduction to Matplotlib
- Understanding PyPlot and Inline Usage
- Anatomy of a Figure
- Using Backends in Matplotlib
- Creating 2D Plots
- Creating 3D Plots
Scientific Computing with SciPy
- Introduction to SciPy
- Integration and Solving ODEs
- Linear Algebra with SciPy
- Optimization Techniques
- Interpolation Methods
Data Processing for Machine Learning
- Introduction to Data Processing
- Data Collection and Acquisition
- Data Cleaning and Preprocessing
- Feature Engineering and Transformation
- Handling Missing Data
- Scaling and Normalization
- Encoding Categorical Data
- Outlier Detection and Handling
Machine Learning Model Development
- Model Selection and Training
- Overfitting and Underfitting
- Learning Rate and Optimization
- Confusion Matrix and Evaluation Metrics
- Understanding Model Bias
Deep Learning with PyTorch
- Getting Started with PyTorch
- Tensor Operations in PyTorch
- Building a Basic Neural Network
- Training and Evaluating a Model
- Image Classification with PyTorch
Deep Learning with TensorFlow
- Introduction to TensorFlow
- Tensor Operations in TensorFlow
- Building a Basic Neural Network
- Training and Evaluating Models
- Image Classification with TensorFlow
ML Deployment with HuggingFace and Gradio
- Introduction to Transformers
- Overview of the HuggingFace Platform
- Introduction to Gradio
- Integrating Gradio with HuggingFace Models
- Hosting ML Applications on HuggingFace
- Troubleshooting and Fixing Deployment Errors
Special Guest Appearances
Mahesh Chand
CEO at C# Corner
Anoop Jain
CEO at gNxt Systems
LIVE Project (Dev-Ops included)
Coming soon
Your Instructor
Meet Your Instructor
Hi! I'm Rohit Gupta, a dedicated and analytical professional with extensive experience in Web3 and currently working as a Technical Lead at CSharp Inc. My passion lies in crafting innovative solutions in Generative AI, Smart Systems, and Personal Language Models. As a speaker at over 20 national and international AI conferences and author of 30+ technical articles, I strive to bridge the gap between conceptual AI and practical implementation. I’ve also written 3 eBooks on Machine Learning and Python that are available for download, and I’ve had the pleasure of sharing insights as a guest on The Guiding Voice Podcast. I believe in human-AI synergy — “Bonding between human and machine will make the world a better place.” The more we understand and analyze our data, the more intelligently we shape our future.
500+
Students taught
400+
Students placed
7+
Years of experience
Outcomes after training
Upon successful completion of the AI Developer Training program, learners can expect to achieve the following career-enhancing outcomes:
- Achieve Salary Milestones – Up to ₹24 LPA in AI & Machine Learning Roles
- Work with Fortune 500 Companies
- Build Advanced AI Solutions – Develop real-world AI systems
- Master In-Demand Skills – Python, TensorFlow, PyTorch & more
- Work on Practical Projects – Hands-on experience with AI applications
- Enhance Interview Success – Prepare for top AI roles
- Deploy AI Models – Use tools like Gradio and HuggingFace
Companies Hiring this Skill
Empowering Minds, Transforming Futures: Your Journey to Tech Excellence Begins Here!