AI & Machine Learning Mastery: From Zero to Practitioner

Courses in this academy

Academy lesson outline

1. What Is Artificial Intelligence — And Why Does It Matter Now? · 13 min
2. Machine Learning vs. AI — What's the Difference and Why It Matters · 12 min
3. A Brief History of AI — From Turing to ChatGPT · 13 min
4. How AI Is Already Transforming Industries — And What That Means for You · 14 min
5. The Language of AI — 30 Terms Every Practitioner Must Know · 14 min
6. Types of Machine Learning — Supervised, Unsupervised, and Reinforcement · 13 min
7. The Data-Driven Mindset — Why Data Is the Foundation of All AI · 13 min
8. The AI Development Lifecycle — From Problem to Production · 14 min
9. AI Ethics and Responsible AI — What Every Practitioner Must Know · 14 min
10. Your AI Learning Roadmap — How to Go From Zero to Practitioner · 15 min
11. How Machines Actually Learn — The Core Idea Behind Every Algorithm · 13 min
12. Gradient Descent — How Models Find the Best Answer · 13 min
13. Decision Trees — Making Predictions by Asking Questions · 12 min
14. Random Forests — Why Many Weak Models Beat One Strong Model · 13 min
15. The Bias-Variance Tradeoff — The Most Important Balance in ML · 14 min
16. Cross-Validation — Getting Reliable Performance Estimates · 12 min
17. Regularisation — Preventing Overfitting Without Losing Power · 13 min
18. Hyperparameter Tuning — Finding the Best Settings for Your Model · 13 min
19. Ensemble Methods — Boosting and Stacking · 14 min
20. Feature Engineering — Turning Raw Data Into Model Intelligence · 15 min
21. Why Python? The Language That Runs the AI World · 12 min
22. pandas Essentials — Mastering Data Wrangling for ML · 13 min
23. Data Visualisation — Seeing What Your Data Is Telling You · 12 min
24. Your First Classification Model — End-to-End in scikit-learn · 14 min
25. Model Evaluation Deep Dive — Metrics That Actually Matter · 14 min
26. Your First Regression Model — Predicting Continuous Values · 13 min
27. scikit-learn Pipelines — Production-Ready ML From Day One · 14 min
28. Handling Class Imbalance — When One Class Is Rare · 13 min
29. Hyperparameter Tuning in Practice — From Grid Search to Optuna · 13 min
30. Deploying Your First ML Model — From Notebook to API · 15 min
31. Data Collection and Sourcing — Finding the Data You Need · 13 min
32. Data Quality — Auditing, Diagnosing, and Fixing Real-World Problems · 13 min
33. Outlier Detection and Treatment — Managing Extreme Values · 12 min
34. Advanced Encoding — Handling High-Cardinality Categoricals · 13 min
35. Scaling and Normalisation — Matching Transformations to Distributions · 12 min
36. Feature Selection — Removing What Hurts, Keeping What Helps · 13 min
37. Building a Complete Data Preparation Pipeline — Nigerian Fintech Use Case · 14 min
38. Time Series Data — Special Rules for Sequential Predictions · 14 min
39. Data Augmentation — Creating More Training Data When You Have Too Little · 12 min
40. Data Ethics and Privacy in Nigerian ML — NDPA 2023 and Responsible Data Use · 14 min
41. Logistic Regression — The Workhorse of Classification · 13 min
42. k-Nearest Neighbours — Learning by Analogy · 12 min
43. Support Vector Machines — Maximum Margin Classification · 13 min
44. Naive Bayes — Fast, Simple, and Surprisingly Effective · 12 min
45. Model Comparison Framework — How to Choose the Best Algorithm · 14 min
46. Probability Calibration — Making Model Probabilities Trustworthy · 13 min
47. Multi-class Classification — When There Are More Than Two Outcomes · 13 min
48. Regression Algorithms Deep Dive — Ridge, Lasso, and Beyond · 13 min
49. Learning Curves and Model Diagnostics — Identifying What Is Wrong · 14 min
50. Module 5 Capstone — Supervised Learning Comparison on a Nigerian Dataset · 15 min
51. k-Means Clustering: Finding Hidden Groups in Your Data · 13 min
52. Hierarchical Clustering: Building a Map of Your Data · 12 min
53. DBSCAN: Detecting Anomalies and Non-Spherical Clusters · 14 min
54. PCA — Shrinking Your Data Without Losing the Signal · 13 min
55. t-SNE and UMAP — Visualising Complex Data in 2D · 13 min
56. Association Rules and Market Basket Analysis · 13 min
57. Collaborative Filtering — How Recommendation Engines Know What You Want · 14 min
58. Content-Based Filtering — Recommending by What Things Are · 12 min
59. Hybrid Recommendation Systems — Combining Signals for Superior Results · 14 min
60. Unsupervised Learning Capstone — Nigerian Market Segmentation · 15 min
61. The Perceptron and Multi-Layer Perceptrons: Building the Brain's Blueprint · 13 min
62. Backpropagation: How Neural Networks Learn · 14 min
63. Activation Functions Deep Dive: Choosing the Right Spark · 12 min
64. PyTorch Fundamentals: Your Deep Learning Workshop · 14 min
65. Building Your First Neural Network in PyTorch · 15 min
66. Regularisation for Neural Networks: Preventing Overfitting · 13 min
67. CNNs for Image Classification: Architecture Intuition · 14 min
68. Transfer Learning with Pre-Trained Models · 15 min
69. RNNs and LSTMs for Sequential Data · 14 min
70. Neural Network Architecture Selection Guide · 13 min
71. How Large Language Models Work: Transformer Architecture Intuition · 14 min
72. Prompt Engineering for Practitioners · 14 min
73. Building With the Anthropic and OpenAI APIs · 13 min
74. RAG Systems: Giving LLMs Access to Your Data · 15 min
75. Vector Databases and Semantic Search · 13 min
76. Fine-Tuning vs. RAG vs. Prompting: Choosing the Right Approach · 13 min
77. AI Agents With Tool Use · 14 min
78. Nigerian Business Automation With AI · 14 min
79. LLM Evaluation and Hallucination Mitigation · 13 min
80. Module 8 Capstone — Building an AI-Powered Nigerian Business Tool · 15 min
81. Model Serving Architectures · 13 min
82. FastAPI Deployment Deep Dive · 14 min
83. Docker for ML Model Packaging · 13 min
84. Monitoring ML Models in Production · 13 min
85. Model Versioning and Experiment Tracking With MLflow · 13 min
86. CI/CD for ML Pipelines · 13 min
87. Cost Optimisation for ML Inference at Scale · 13 min
88. A/B Testing ML Models in Production · 14 min
89. Scaling ML Systems · 13 min
90. MLOps Capstone — Deploying a Complete Nigerian Fintech ML System · 15 min
91. AI Strategy for Nigerian Organisations · 14 min
92. AI ROI Calculation and Business Case Building · 13 min
93. AI Ethics — Bias, Fairness, and NDPA 2023 · 14 min
94. Responsible AI Deployment Checklist · 12 min
95. AI Career Paths for Nigerian Practitioners · 13 min
96. Building Your AI Portfolio · 12 min
97. Freelancing and Consulting With AI Skills in Nigeria · 13 min
98. AI and the Future of Nigerian Industries · 13 min
99. Building a Personal AI Learning System · 12 min
100. AI Mastery Capstone — Your 90-Day AI Action Plan · 15 min

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