Master Artificial Intelligence & Machine Learning
Master AI & ML from fundamentals to advanced: Python, NumPy, Pandas, data visualization, statistics, supervised & unsupervised learning, regression, classification, clustering, NLP, reinforcement learning, and real-world projects. Build intelligent systems and become an AI engineer.
Mohammad Shahid
Full Stack Developer & AI Expert | ML Engineer | 5+ Years Teaching Experience
About This Classroom Program
Master AI & ML from scratch with Python, NLP, and model deployment. Build real-world projects like recommendation systems, image classifiers, and stock price predictors to become a job-ready data scientist or ML engineer.
Program Duration
Class Size
Batch Options
Location
What You'll Learn
- Master Python programming for data science and ML
- Use NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization
- Understand fundamental statistics and probability for ML
- Perform data cleaning, preprocessing, and feature engineering
- Implement supervised learning algorithms (regression, classification)
- Implement unsupervised learning algorithms (clustering, dimensionality reduction)
- Build ensemble models (Random Forest, Gradient Boosting, XGBoost)
- Master Natural Language Processing (NLP) - tokenization, embeddings, sentiment analysis, transformers
- Develop deep learning models using TensorFlow and PyTorch
- Build Convolutional Neural Networks (CNNs) for computer vision
- Build Recurrent Neural Networks (RNNs/LSTMs) for sequence data and time series
- Understand transfer learning and fine-tuning pre-trained models
- Implement reinforcement learning concepts (Q-learning, policy gradients)
- Deploy ML models using Flask, FastAPI, Docker, and cloud services (AWS/GCP)
- Work with MLOps tools (MLflow, DVC, Kubeflow) for model lifecycle management
- Build 10+ real-world projects including recommendation engine, fraud detection, face recognition, chatbot, and more
Prerequisites
- Basic computer knowledge
- No prior AI/ML experience required
- Basic mathematics (high school level algebra and calculus) is helpful but not mandatory
- A computer with 8GB+ RAM (16GB recommended) and internet connection
- Eagerness to learn coding and algorithms
Classroom Session Plan
Hands‑on, in‑person sessions led by expert instructors
Why Choose In‑Person Learning?
Peer Collaboration
Work in groups, share ideas, and learn from diverse perspectives.
Instant Doubt Resolution
Get real‑time answers from instructors and peers.
Campus Experience
State‑of‑the‑art labs, dedicated study spaces, and networking.
How to Join
Fill Inquiry
Submit your interest via the form
Counseling & Assessment
Get guidance and take a skills assessment
Enroll & Start
Complete admission and begin your journey
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