Artificial Intelligence
Learn AI with hands-on projects & mentorship
⭐ 4.8 (100 reviews)👥 500 students⏱ 10-12 Weeks📈 Beginner → Advanced
Hands-on Projects
Hybrid Mentorship
Certificate
Portfolio
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
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
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