Building Intelligent Mobile Apps using Machine Learning
summary
Quick primer on what machine learning actually does with live demos and tools: hands-on k‑means clustering of a 9,000‑person body‑measurements CSV using Python (Anaconda, NumPy, pandas, matplotlib) and scikit‑learn; deploying a Titanic two‑class classifier in Azure Machine Learning Studio and comparing AUCs (~0.849 vs 0.801); calling Vision and Face REST APIs and training Custom Vision to recognize durian with ~99.97% confidence, then export to CoreML for on‑device use. Practical recipes, code libraries, dataset sizes and measured results, and a working web service deployment.
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chapters (12)
- 0:00Start
- 9:20What is Machine Learning?
- 9:58Why Machine Learning?
- 10:31Uses of Machine Learning
- 12:57Classification
- 15:00Clustering
- 15:38Anomaly Detection
- 16:04Reinforcement Learning
- 17:03Supervised Learning
- 21:47Logistic Regression
- 24:03Support Vector Machine
- 34:44Python Demo
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