Introduction to Machine Learning 3 introduction to Machine Learning types of Machine Learning Find out where Machine Learning is applied in Technology and Science. Supervised Learning and Linear Regression 3 types of supervised learning, introduction of regression and classification simple linear regression & multiple linear regression, Introduction to classification 3 linear regression vs logistic regression math behind in logistic regression Implementing logistic regression from scratch with Python Support Vector Machines 3 Learn the simple intuition behind Support Vector Machines. mplement an SVM classifier in SKLearn/scikit-learn. Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels. Introduction of Decision Trees & Random Forest 3 Code your own decision tree in python Learn the formulas for entropy and information gain and how to calculate them. Implement a mini project where you identify the authors in a body of emails using a decision tree in Python. Introduction of Clustering 3 Identify the difference between Unsupervised Learning and Supervised Learning. Implement K-Means in Python and Scikit Learn to find the center of clusters. Apply your knowledge on the Enron Finance Data to find clusters in a real dataset. Machine Learning workshop Back to CourseThis content is protected, please login and enroll course to view this content! Prev simple linear regression & multiple linear regression, Next math behind in logistic regression Leave a Reply Cancel replyYour email address will not be published. Required fields are marked *Name * Email * Website Comment