Machine Learning and Deep Learning With Spark

Course Objectives

• Basic concepts in statistical learning
• Basics of Spark
• Understanding of classification algorithms decision tree, naive Bayes, logistic regression and support vector machine
• Knowledge of simple regression algorithms such as linear regression and decision tree-based–regression
• Understanding of unsupervised learning algorithm: K-means clustering and principle component analysis
• Ability to use machine-learning libraries provided by Spark/MLLib using the Python, Scala or R interfaces
• Understanding of basic concepts of deep learning using TensorFlow

What You Learn

• Machine learning concepts
• Spark
• Resilient Distributed Datasets and MapReduce
• Recommendation algorithms
• Classification algorithms: logistic regression, support vector machine, decision trees, naive Bayes
• Regression algorithms: least square, decision tree
• Clustering algorithms: K-means clustering
• Principle component analysis
• Dimensionality reduction: principle component analysis and singular vector decomposition
• Machine-learning libraries
• Deep learning fundamentals
• TensorFlow

How You Learn

• Lectures
• Demonstrations
• Hands-on exercises
• Group study
• Homework assignments
• In-class quizzes and exams
• Final project

Is This Course Right for You?

The course is intended for students or IT professionals who would like to gain basic knowledge and hands-on experience of machine learning