Zero knowledge → degree → professional → expert

AI & Data Science

Data, machine learning, deep learning and analytics

107 structured topics, each reinforced with plain-language teaching, memory hooks, hands-on practice, retrieval, teach-back, speed recall, professional transfer and spaced review.

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How mastery works

1. Five-year-old simple

Every new term is explained without assumed knowledge and linked to a familiar picture.

2. Do it

Guided labs, typing, diagrams, configurations, queries or professional artefacts turn words into usable skill.

3. Retrieve it

No-peeking recall, teach-back and 60-second checks force the brain to retrieve instead of recognise.

4. Use it professionally

Failure modes, security, evidence, capstones and spaced repetition build degree and workplace fluency.

Course topics

  1. What is AI? — Artificial intelligence overview
  2. History of AI — From Turing to modern AI
  3. Types of AI — Narrow, general, superintelligence
  4. AI Applications — Real-world use cases
  5. Machine Learning Intro — Learning from data
  6. Types of ML — Supervised, unsupervised, reinforcement
  7. Supervised Learning — Labeled data training
  8. Unsupervised Learning — Pattern discovery
  9. Reinforcement Learning — Learning from rewards
  10. Data Collection — Sources, quality, quantity
  11. Data Types — Numerical, categorical, text
  12. Data Cleaning — Missing values, outliers
  13. Normalization — Scaling and standardization
  14. Feature Engineering — Creating useful features
  15. Exploratory Data Analysis — Understanding your data
  16. Data Visualization Intro — Why visualize data
  17. Chart Types — Bar, line, scatter, pie
  18. Statistical Plots — Histograms, box plots
  19. Heatmaps & Correlation — Visualizing relationships
  20. Building Dashboards — Interactive visualizations
  21. Linear Regression — Predicting continuous values
  22. Multiple Regression — Multiple predictors
  23. Polynomial Regression — Non-linear relationships
  24. Regularization — L1 and L2 penalties
  25. Regression Metrics — MSE, RMSE, R²
  26. Classification Basics — Predicting categories
  27. Logistic Regression — Binary classification
  28. K-Nearest Neighbors — Instance-based learning
  29. Naive Bayes — Probabilistic classifier
  30. Support Vector Machines — Maximum margin classifier
  31. Classification Metrics — Accuracy, precision, recall, F1
  32. Decision Trees — Tree-based learning
  33. Tree Pruning — Preventing overfitting
  34. Random Forests — Ensemble of trees
  35. Gradient Boosting — XGBoost, LightGBM
  36. Ensemble Methods — Bagging vs boosting
  37. Clustering Introduction — Grouping similar data
  38. K-Means Clustering — Centroid-based clustering
  39. Hierarchical Clustering — Dendrograms
  40. DBSCAN — Density-based clustering
  41. Cluster Evaluation — Silhouette score
  42. Train/Test Split — Validation strategies
  43. Cross-Validation — K-fold validation
  44. Overfitting — Model too complex
  45. Underfitting — Model too simple
  46. Bias-Variance Tradeoff — Finding the balance
  47. Hyperparameter Tuning — Grid and random search
  48. Neural Networks Intro — Neurons and layers
  49. Perceptron — Single neuron model
  50. Multilayer Perceptron — Hidden layers
  51. Activation Functions — ReLU, sigmoid, tanh
  52. Backpropagation — Training neural networks
  53. Deep Learning Intro — Many hidden layers
  54. Convolutional NNs — Image processing
  55. Recurrent NNs — Sequential data
  56. LSTM Networks — Long-term dependencies
  57. Transfer Learning — Reusing trained models
  58. Ethics in AI — Bias, fairness, transparency
  59. AI Bias — Sources and mitigation
  60. Explainable AI — Interpretable models
  61. AI in Industry — Real-world applications
  62. Model Deployment — Production systems
  63. NLP Introduction — Processing human language
  64. Text Preprocessing — Tokenization, stemming, lemmatization
  65. Word Embeddings — Word2Vec, GloVe, FastText
  66. Transformers — Attention mechanism and BERT
  67. Large Language Models — GPT, LLaMA, fine-tuning
  68. Prompt Engineering — Effective AI prompting
  69. Computer Vision Intro — Image understanding
  70. Image Processing — Filters, edges, transformations
  71. Object Detection — YOLO, SSD, R-CNN
  72. Image Segmentation — Semantic and instance
  73. GANs — Generative Adversarial Networks
  74. Robotics Introduction — Robots and automation
  75. Robot Kinematics — Movement and positioning
  76. Sensors & Actuators — Robot perception and action
  77. Robot Programming — ROS and control systems
  78. Autonomous Systems — Self-driving and drones
  79. IoT & Edge AI — AI on embedded devices
  80. MLOps Introduction — ML in production
  81. ML Pipelines — Automated workflows
  82. Model Monitoring — Drift detection and retraining
  83. Data Engineering — ETL, data lakes, warehouses
  84. Apache Spark Basics — Big data processing
  85. Python for Data Science — NumPy, Pandas, Matplotlib
  86. R Programming Basics — Statistical computing
  87. AI Chatbot Creation — Building conversational AI
  88. GPT Fundamentals — How GPT models work
  89. Custom GPT Builder — Creating custom GPTs in ChatGPT
  90. Fine-Tuning Models — Training on custom data
  91. RAG Systems — Retrieval-Augmented Generation
  92. AI API Integration — OpenAI, Anthropic, Google APIs
  93. LangChain Framework — Building AI applications
  94. Vector Databases — Pinecone, Weaviate, ChromaDB
  95. AI Agents — Autonomous AI systems
  96. Data Analytics Introduction — Turning data into insights
  97. SQL for Analytics — Advanced queries for analysis
  98. Excel for Analytics — Pivot tables, VLOOKUP, macros
  99. Tableau & Power BI — Visual analytics tools
  100. A/B Testing — Experiment design and analysis
  101. Predictive Analytics — Forecasting with data
  102. Robotics Hardware — Arduino, Raspberry Pi, servos
  103. Advanced Sensors — LiDAR, IMU, ultrasonic
  104. Robot Vision Systems — Camera-based perception
  105. Robot Manipulation — Grippers and arm control
  106. Drone Programming — UAV flight control
  107. Industrial Automation — PLC, SCADA, Industry 4.0

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