# 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. 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. https://youtu.be/GNgo1846F7U 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 https://youtu.be/Q0WyK89bTLY 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. https://youtu.be/HYcXgN9HaTM 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. https://youtu.be/qZEZBvBRs98