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 math behind in logistic regression Next Learn the simple intuition behind Support Vector Machines. Leave a Reply Cancel replyYour email address will not be published. Required fields are marked *Name * Email * Website Comment
