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Machine Learning is so pervasive today which is the science of getting computers to act without hard coding. Many improvisations in our lifestyle have changed vastly after the intervention of ML, like the past decade saw practical speech recognition, effective web search and so on. This course takes you on learning over Supervised learning, ML based Vs Rule based concepts, classification using Support vector Machine, Decision Trees and Random Forests.
SVM and Decision Trees
Random Forest
Concepts under Supervised Learning
Deploy data that allows user to gather precise information from it
N/A
Students, graduates, working professionals and programmer.
Basic knowledge of programming.
About Machine Learning Course
Installation of Anaconda
What is Machine Learning
Types of Machine Learning, Supervised Learning and Regression
Types of ML,Logistic Regression and Unsupervised Learning
SVM -What is SVM and How do they work
SVM-Loading and Examining our dataset
SVM-Building and Tweaking our SVM Classification mode
What is Decision Tree?
Building the Decision Tree : Decision Tree Learning
Building a Decision Tree - Information Gain a Gini Impurity
Decision Tree Lab:Building our First Decision Tree
Decision Tree Lab:Viewing and Tweaking our Decision Tree
What is Overfitting
Random Forest Lab
Teamwork
Avoiding Overfitted Models
Training Provider
