Artificial Intelligence (4-Month Course)
Become a professional Artificial Intelligence Engineer by learning the most in-demand AI technologies used across the industry. This intensive 4-month hands-on course takes you from Python programming fundamentals to building intelligent AI applications using Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and Generative AI. You'll work on real-world projects, train AI models, deploy them to the cloud, and gain practical skills required for internships, freelancing, research, and AI careers.
Technologies You'll Learn
- Python Programming
- NumPy & Pandas
- Data Visualization (Matplotlib & Seaborn)
- SQL for AI
- Machine Learning
- Scikit-learn
- Deep Learning
- TensorFlow & Keras
- PyTorch
- Computer Vision (OpenCV)
- Natural Language Processing (NLP)
- Generative AI
- Prompt Engineering
- Large Language Models (LLMs)
- LangChain
- Hugging Face
- Git & GitHub
- Streamlit
- FastAPI
- AI Model Deployment (Cloud)
What You'll Learn
- Master Python programming for Artificial Intelligence.
- Perform data cleaning, preprocessing, and visualization.
- Build Machine Learning models for prediction and classification.
- Train Deep Learning models using TensorFlow and PyTorch.
- Develop Computer Vision applications using OpenCV.
- Build NLP applications for text analysis and chatbots.
- Create AI-powered applications using Generative AI and LLMs.
- Learn Prompt Engineering for ChatGPT and other AI models.
- Build AI APIs using FastAPI.
- Deploy AI models and applications using Streamlit and cloud platforms.
- Use Git and GitHub for version control.
- Complete multiple industry-level AI projects.
4-Month Course Roadmap
Month 1 – Python & Data Science Foundations
- Week 1: Introduction to AI, Python Basics, VS Code Setup, Variables, Data Types
- Week 2: Loops, Functions, OOP, File Handling, Exception Handling
- Week 3: NumPy, Pandas, Data Cleaning
- Week 4: Data Visualization, SQL Basics, Mini Project
Month 2 – Machine Learning
- Week 5: Machine Learning Fundamentals, Supervised & Unsupervised Learning
- Week 6: Regression Algorithms, Classification Algorithms
- Week 7: Model Evaluation, Feature Engineering
- Week 8: Decision Trees, Random Forest, SVM, Clustering, Recommendation System Project
Month 3 – Deep Learning, Computer Vision & NLP
- Week 9: Neural Networks, TensorFlow, Keras
- Week 10: CNN, Image Classification, Object Detection using OpenCV
- Week 11: NLP Fundamentals, Text Processing, Sentiment Analysis
- Week 12: RNN, Transformers, Hugging Face, Chatbot Development
Month 4 – Generative AI & Deployment
- Week 13: Generative AI, LLMs, Prompt Engineering
- Week 14: LangChain, RAG Applications, AI Agents
- Week 15: FastAPI, Streamlit, Model Deployment
- Week 16: Final AI Project, GitHub Portfolio, Cloud Deployment & Interview Preparation
Final Project
By the end of the course, students will build and deploy a complete AI-powered application using Machine Learning, Deep Learning, Computer Vision, NLP, or Generative AI. The project will include data preprocessing, model training, evaluation, API integration, deployment, and a professional GitHub portfolio ready to showcase to employers and clients.
Who Should Enroll?
- Complete beginners with no prior programming experience.
- College and university students.
- Aspiring AI and Machine Learning Engineers.
- Software developers who want to transition into AI.
- Freelancers interested in AI services.
- Data science enthusiasts.
- Anyone looking to build a career in Artificial Intelligence.
Career Opportunities
- Artificial Intelligence Engineer
- Machine Learning Engineer
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- Generative AI Engineer
- AI Application Developer
- Data Scientist
- AI Research Assistant
- AI Automation Engineer
- Prompt Engineer
- AI Consultant
- Freelance AI Developer