COURSE DESCRIPTION

Machine Learning Classification is one of the most popular research areas in the machine learning field. In this course, you will learn about many common and widely used machine learning classification algorithms like Decision Tree, Random Forest, SVM or Neural Network. In addition, you will have a chance to apply them to solve real world problems like Sentiment Analysis, Fraud Detection and Image Classification. You will learn each algorithm one by one from easy to hard and apply them to real-world datasets. You will also learn how to evaluate classification models and use them in appropriate problems.

LEARNING OUTCOMES

• Know what classification problems in Machine Learning are
• Have knowledge about Linear Classifiers & Logistic Regression model, how they are learned using gradient ascent and apply them to real word examples and datasets
• Have knowledge about overfitting and regularization in classification and how to prevent them
• Gain knowledge about Decision Tree model, Boosting model, SVM, Naive Bayes, Random Forest, Feed forward Neural Network. Apply them to real word examples and datasets
• Have knowledge about precision and recall. Know when to use them for different problems
• Have knowledge about methods for a huge dataset. Use them for real word examples
• Apply all classification algorithms in the course to a real world problem and dataset