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Berkeley Global
Machine learning plays an important role in big data analytics. In this introductory course, you learn the basic concepts of different machine-learning algorithms, answering such questions as when to use an algorithm, how to use it and what to pay attention to when using it. You use Apache Spark—an open-source cluster computing framework that is garnering significant attention in the data industry—as the primary platform for implementing these algorithms. The course curriculum minimizes mathematical derivations in favor of hands-on mastery of Spark's data-processing and streaming features. You also get an introduction to deep learning fundamentals and hands-on experience of deep learning with TensorFlow.
Prerequisites:
Required
- Knowledge of statistics as covered in a first semester undergraduate course. Need to fulfill this prereq? Take a course in:
- Introduction to Statistics STAT X10
- Introduction to Statistics STAT X10
- Ability to program in at least one high-level programming language. Python or Scala are preferred. C/C++ acceptable. Need to fulfill this prereq? Take a course in:
- Programming Python COMPSCI X434
- First Course in Java EL ENG X429.9
- Introduction to C Language Programming EL ENG X24
- C++ Programming EL ENG X412.1
Course Outline
Expand or collapse section
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
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