Welcome to Machine Learning 101. Please find the course outline below. Get the videos as well
Lecture # | Description | Watch Video |
1 | Introduction to Machine Learning 101 Course – Welcome to Machine Learning 101! Prerequisite for this course. How the course is arranged. Applications you need. Nuggets. Procedure based. | 1. https://lnkd.in/dA9AMhR |
2 | Overview of Machine Learning and Some Basic Terms – What is Machine Learning? Application areas of Machine learning. How Machine learning works. Traditional programming vs machine learning. Hand-written digits example. Some terms: training set, target vector. Training or Learning. Test data set. Preprocessing, Feature extraction | 2. https://lnkd.in/etaqdy5 |
3 | Classes of Machine Learning Problems – Supervised Learning, Unsupervised Learning and Reinforcement Learning. Classification and Regression. Clustering, Density estimation and dimensionality reduction. Credit Assignment | 3. https://lnkd.in/dbYidEm |
4 | How to Solve Simple Regression Problem – What is Regression? Marketing Ads and profits example. Regression by inspection. Types of Regression. How to plot in Python | 4. https://bit.ly/2W6mG3u |
5 | Equation of a Regression Line – Regression example. Determining trend in data. Equation of a straight line. Slope and intercept. Regression in Python | 5. https://youtu.be/O0PNXhipbf4 |
6 | Polynomial Curve Fitting – Review of regression. Polynomial coefficients, w. Determining w. Error function | 6. https://bit.ly/2Po1hQT |
7 | Overfitting and Underfitting – Reveiw of polynomial curve fitting. Increasing the order M, of the polynomial. How M relates to the error. M being too low vs too high. Trade-off point. Python hands-on | 7. https://bit.ly/2W5jkxQ |
8 | Introduction to Probability Theory – Basics of probability. Simple example. Experiments and Random variable. Some common notations. Some quiz. | 8. https://bit.ly/2Zu4MtD |
9 | Rule of Probability and Bayes’ Theorem – Marginal Probability. Joint Probability. Conditional Probability. Sum Rule. Product rule. Deriving Bayes’ theorem. Formulas | 9. https://bit.ly/2XGVx7H |
10 | Application of Bayes’ Theorem in Real Scenario – Scenario problem of finding conditional, marginal and joint probabilities. Using sum rule, product rule and Bayes’ Rule. Exercises. Prio probability. Posterior probability. Independence | 10. https://bit.ly/2GCeHpJ |
11 | What is Probability Density – Continuous variable versus discreet variable, the differences. Probability distribution. Random variable. Probability density. Probability Density Function. CDF and PMF. | 11. https://bit.ly/2ZvSAIU |
12 | Bias/Variance Trade-off – What is Bias-Variance Trade-off? Mean-Square-Error(MSE) formula. Decomposition of MSE. Meaning of Bias and Variance. Model complexity and flexibility. Bias/Variance Trade-off graph. Review of Overfitting and Underfitting | 12. https://bit.ly/2ZutnOT |
13 | Introduction to Classification – What is Classification? Mean-Square-Error(MSE). Error Rate. Typical classification problem. Cancer diagnosis. Inference an decision. Short Quiz. | 13. https://youtu.be/_qjvpJiYPB8 |
14 | The Bayes’ Classifier and How it Works – How the Bayes’ Classifier works. Bayes’ theorem. | 14.https://youtu.be/vKh8bbxplcg |
15 | K-Nearest Neighbors Classifier – How K-nearest neighbor classifier works. Contrast with Bayes’ classifier. Illustration of KNN. Algorithm of KNN. | |
16 | Minimizing Misclassification Rate in Bayes’ Classifier – Review of Bayes’ Classifier. Review of cancer diagnosis example in classification. Review of Conditional probability. Misclassification and misclassification rate. Minimizing misclassification. Decision boundaries. Review of product rule in probability | |
17 | Basics of Logistic Regression – Review of Classes of Machine Learning Problems. What is Logistic Regression? Probability Distribution. Logistic Function. Odds and Odds Ratio. Meaning of odds. Logit or log-odds. | |
18 | Introduction to Multiple Linear Regression – The advertising dataset. Importing data to Jupyter Notebook. Determining how predictor variables affect the response variable. Calculating regression coefficients. How multiple regression works. Some python practicals. |