Artificial Intelligence

Learn AI with hands-on projects & mentorship

4.8 (100 reviews)👥 500 students10-12 Weeks📈 Beginner → Advanced
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Artificial Intelligence
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

Ready to start learning?

Join thousands of learners building career-ready AI skills.