Machine Learning 101 Complete Course

Welcome to Machine Learning 101

Please find the course outline below. Get the videos as well

Lecture #DescriptionWatch Video
1Introduction 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.
2Overview 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 extraction2.
3Classes of Machine Learning Problems – Supervised Learning, Unsupervised Learning and Reinforcement Learning. Classification and Regression. Clustering, Density estimation and dimensionality reduction. Credit Assignment3.
4How to Solve Simple Regression Problem – What is Regression? Marketing Ads and profits example. Regression by inspection. Types of Regression. How to plot in Python4.


5Equation of a Regression Line – Regression example. Determining trend in data. Equation of a straight line. Slope and intercept. Regression in Python5.
6Polynomial Curve Fitting – Review of regression. Polynomial coefficients, w. Determining w. Error function6.
7Overfitting 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-on7.


8Introduction to Probability Theory – Basics of probability. Simple example. Experiments and Random variable. Some common notations. Some quiz.8.
9Rule of Probability and Bayes’ Theorem – Marginal Probability. Joint Probability. Conditional Probability. Sum Rule. Product rule.  Deriving Bayes’ theorem. Formulas9.
10Application 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. Independence10.


11What is Probability Density – Continuous variable versus discreet variable, the differences. Probability distribution. Random variable.  Probability density. Probability Density Function. CDF and PMF.11.
12Bias/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 Underfitting12.
13Introduction to Classification – What is Classification? Mean-Square-Error(MSE). Error Rate. Typical classification problem. Cancer diagnosis. Inference an decision. Short Quiz.13.


14The Bayes’ Classifier and How it Works – How the Bayes’ Classifier works. Bayes’ theorem.14.
15K-Nearest Neighbors Classifier – How K-nearest neighbor classifier works. Contrast with Bayes’ classifier. Illustration of KNN. Algorithm of KNN.
16Minimizing 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
17Basics 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.
18Introduction 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.