Machine Learning 101 Complete Course

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.
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.
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.
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.


5 Equation of a Regression Line – Regression example. Determining trend in data. Equation of a straight line. Slope and intercept. Regression in Python 5.
6 Polynomial Curve Fitting – Review of regression. Polynomial coefficients, w. Determining w. Error function 6.
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.


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


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.
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.
13 Introduction to Classification – What is Classification? Mean-Square-Error(MSE). Error Rate. Typical classification problem. Cancer diagnosis. Inference an decision. Short Quiz. 13.


14 The Bayes’ Classifier and How it Works – How the Bayes’ Classifier works. Bayes’ theorem. 14.
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.