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AI & Machine Learning Complete

The most comprehensive AI/ML course - from fundamentals to deep learning, transformers and building with AI APIs.

SU Taught by Sukhwinder Singh 0 enrolled Advanced

What you will learn

Master Artificial Intelligence and Machine Learning - the most transformative technology of the decade.

Covers AI fundamentals, Python for ML, statistics, data preprocessing, regression, classification, clustering, neural networks, deep learning with TensorFlow, transformers, prompt engineering, AI APIs and real-world projects.

Who this is for:
- Python developers wanting to enter AI/ML
- Data analysts looking to add machine learning
- Students pursuing AI engineering careers
- Anyone building intelligent applications

Course Syllabus

0 lessons · 13 modules
1. AI Fundamentals 4 lessons
  1. Lesson content for "What is AI, ML, Deep Learning - the hierarchy".

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  2. Lesson content for "Types of ML - supervised, unsupervised, reinforcement".

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  3. Lesson content for "Real-world AI applications in 2026".

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  4. Lesson content for "Setting up your ML environment - Jupyter, Colab".

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2. Python for Machine Learning 5 lessons
  1. Lesson content for "NumPy for ML - vectors, matrices, operations".

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  2. Lesson content for "Pandas for feature engineering".

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  3. Lesson content for "Matplotlib and Seaborn for ML visualization".

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  4. Lesson content for "Scikit-learn introduction and pipeline".

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  5. Lesson content for "Jupyter notebook workflows".

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3. Statistics & Probability for ML 5 lessons
  1. Lesson content for "Descriptive stats and distributions".

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  2. Lesson content for "Probability theory and Bayes theorem".

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  3. Lesson content for "Hypothesis testing and p-values".

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  4. Lesson content for "Correlation, covariance and causation".

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  5. Lesson content for "Linear algebra essentials for ML".

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4. Data Preprocessing 6 lessons
  1. Lesson content for "Handling missing data strategies".

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  2. Lesson content for "Feature scaling - normalization vs standardization".

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  3. Lesson content for "Encoding categorical variables".

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  4. Lesson content for "Feature selection techniques".

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  5. Lesson content for "Train/validation/test splits and cross-validation".

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  6. Lesson content for "Handling imbalanced datasets".

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5. Regression 5 lessons
  1. Lesson content for "Linear regression - theory and implementation".

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  2. Lesson content for "Multiple regression and feature importance".

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  3. Lesson content for "Polynomial regression".

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  4. Lesson content for "Ridge, Lasso and regularization".

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  5. Lesson content for "Model evaluation - MSE, RMSE, R-squared".

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6. Classification 6 lessons
  1. Lesson content for "Logistic regression explained".

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  2. Lesson content for "Decision trees and random forests".

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  3. Lesson content for "Support vector machines (SVM)".

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  4. Lesson content for "K-nearest neighbors (KNN)".

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  5. Lesson content for "Naive Bayes classifier".

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  6. Lesson content for "Evaluation - accuracy, precision, recall, F1, ROC".

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7. Clustering & Unsupervised Learning 5 lessons
  1. Lesson content for "K-Means clustering".

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  2. Lesson content for "Hierarchical clustering and dendrograms".

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  3. Lesson content for "DBSCAN for density-based clustering".

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  4. Lesson content for "Dimensionality reduction with PCA".

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  5. Lesson content for "Evaluating clusters - silhouette score".

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8. Neural Networks Foundations 5 lessons
  1. Lesson content for "Perceptrons and the起源 of neural networks".

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  2. Lesson content for "Activation functions - ReLU, sigmoid, softmax".

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  3. Lesson content for "Forward propagation and backpropagation".

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  4. Lesson content for "Gradient descent and optimizers".

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  5. Lesson content for "Building a neural network with Keras".

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9. Deep Learning with TensorFlow 6 lessons
  1. Lesson content for "TensorFlow and Keras fundamentals".

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  2. Lesson content for "Building and training deep networks".

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  3. Lesson content for "Convolutional Neural Networks (CNNs) for images".

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  4. Lesson content for "Recurrent Neural Networks (RNNs) for sequences".

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  5. Lesson content for "Transfer learning with pre-trained models".

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  6. Lesson content for "Regularization - dropout, batch normalization".

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10. Transformers & Modern AI 4 lessons
  1. Lesson content for "Attention mechanism explained".

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  2. Lesson content for "The Transformer architecture".

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  3. Lesson content for "BERT, GPT and large language models".

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  4. Lesson content for "Fine-tuning pre-trained models".

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11. Prompt Engineering 4 lessons
  1. Lesson content for "What is prompt engineering and why it matters".

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  2. Lesson content for "Prompt techniques - zero-shot, few-shot, chain-of-thought".

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  3. Lesson content for "System prompts and role assignment".

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  4. Lesson content for "Evaluating and iterating on prompts".

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12. AI APIs & Integration 4 lessons
  1. Lesson content for "OpenAI API - chat completions and embeddings".

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  2. Lesson content for "Building a chatbot with the API".

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  3. Lesson content for "RAG - Retrieval Augmented Generation".

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  4. Lesson content for "LangChain for LLM orchestration".

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13. Capstone Projects 4 lessons
  1. Lesson content for "House price prediction - end-to-end ML project".

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  2. Lesson content for "Image classifier with CNNs".

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  3. Lesson content for "Sentiment analysis with NLP".

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  4. Lesson content for "AI-powered web application".

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