Artificial Intelligence
Learn AI with hands-on projects & mentorship
About the Course
Master AI from fundamentals to deployment with mentor-led guidance and portfolio-grade projects. Learn by building, not just watching.
Tip: Hybrid mentorship pairs you with an expert to review code, unlock roadblocks, and plan your portfolio.
Prerequisites
- Basic Python knowledge
- Math & Statistics basics
- Laptop + stable internet
- Curiosity to learn
What You’ll Learn
Build ML models end-to-end
Data preprocessing & feature engineering
Neural networks & training loops
NLP basics & embeddings
Model evaluation & MLOps intro
Deploy models as APIs
Who This Course is For
Great fit for students.
Great fit for freshers.
Great fit for working professionals.
Curriculum
▸Module 1 · Introduction to AI & Applications
- • What is AI? History & Evolution
- • AI vs ML vs DL vs NLP - differences and connections
- • Real-world applications of AI
- • AI lifecycle (data, model, deployment)
- • AI Ethics & Limitations
▸Module 2 · Python for AI
- • Python recap for AI
- • NumPy for arrays & matrices
- • Pandas for data analysis
- • Matplotlib & Seaborn for visualization
- • Scikit-learn introduction
▸Module 3 · Machine Learning Basics
- • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- • Features, labels, datasets
- • Train/test split & cross-validation
- • Performance metrics (accuracy, precision, recall, F1)
- • Hands-on: Linear Regression example
▸Module 4 · Regression & Classification Models
- • Linear Regression
- • Logistic Regression
- • Decision Trees & Random Forests
- • Support Vector Machines (SVM)
- • k-Nearest Neighbors (kNN)
- • Evaluation metrics for regression/classification
▸Module 5 · Clustering & Dimensionality Reduction
- • K-means clustering
- • Hierarchical clustering
- • Principal Component Analysis (PCA)
- • Use cases: segmentation & compression
▸Module 6 · Neural Networks & Deep Learning
- • Neural network basics (perceptrons, activations)
- • Forward & Backpropagation
- • TensorFlow/Keras introduction
- • Build your first ANN
▸Module 7 · Convolutional Neural Networks (CNNs)
- • CNN architecture (filters, pooling, layers)
- • Image classification basics
- • Transfer learning (ResNet, VGG, MobileNet)
- • Project: Cats vs Dogs classifier
▸Module 8 · Natural Language Processing (NLP)
- • Text preprocessing (tokenization, stemming, lemmatization)
- • Bag of Words & TF-IDF
- • Word embeddings (Word2Vec, GloVe)
- • Transformers (BERT, GPT basics)
- • Project: Sentiment Analysis
▸Module 9 · AI Ethics & Responsible AI
- • Bias & Fairness in AI
- • Transparency & Explainability (XAI)
- • AI Governance & Regulations
- • Case study on AI ethics
▸Module 10 · Capstone Project
- • Choose a project (chatbot, fake news detection, image recognition, stock predictor, healthcare AI)
- • Project implementation
- • Project report & presentation
Projects
Intent classification + response flow.
Product recommendation using embeddings.
CNN-based classifier for a custom dataset.
NLP model to classify text as positive, negative, or neutral.
Predict stock trends using ML/DL models.
Disease prediction model using patient dataset.
Testimonials
“This course helped me land interviews fast with AI startups.”
“Mentorship + projects = confidence. Loved every bit of it!”
“From zero coding background to building my own AI models—this course made it possible.”
Instructor
FAQs
Is mentorship included?
Weekly live mentor hours & code reviews.
What prior knowledge do I need for this AI course?
Basic Python programming and understanding of mathematics (linear algebra, probability, and statistics) are recommended but not mandatory. We provide a Python refresher at the start.
How much time should I dedicate each week?
On average, 6–8 hours per week is sufficient to complete lessons, quizzes, and projects.
Will I work on real-world projects?
Yes, each module has mini-projects, and you will complete a capstone project such as building an AI chatbot, image classifier, or sentiment analyzer.
Do I need a powerful computer for deep learning modules?
A laptop with at least 8GB RAM is recommended. For heavy models, we provide guidance on using Google Colab or cloud GPUs for free.
Is this course suitable for beginners?
Yes, the course starts with fundamentals and gradually progresses to advanced AI concepts. Beginners can follow along step by step.