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  • Berkeley Global

Machine Learning and Deep Learning With Spark

COMPSCI X459.5

17065286
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.

Course Outline

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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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Sections

Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please contact the academic department at the email or phone number listed in the Notes section for more information.

This course applies to the following programs:

Advanced Program in Software Development

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Programming Courses

  • Java: Discovering Its Power
  • Mastering Python
  • C++ Programming
  • Data Structures and Algorithms
  • Software Design Patterns

Data and Databases Courses

  • Machine Learning and Deep Learning With Spark
  • Practical Machine Learning (With R)
  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing

Current Topics Category

  • Blockchain Fundamentals
  • Introduction to Quantum Computing With Applications

Elective Courses

  • Introduction to C Language Programming

Learn More About this Program

Certificate Program in Data Science

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Programming

  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing
  • Introduction to Data Science
  • Introduction to Data Science Using R

Machine Learning

  • Introduction to Machine Learning Using Python
  • Machine Learning and Deep Learning With Spark
  • Practical Machine Learning (With R)
  • Machine Learning With TensorFlow

Core Courses

  • Practical Statistics for Data Scientists Using R
  • Introduction to Big Data
  • Data Science Principles and Practice Using Python
  • Data Visualization

Electives

  • Introduction to Databases
  • Introduction to SQL

Learn More About this Program

Notes

Departmental contact: extension-techeng@berkeley.edu | (510) 642-4151

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
       
  • 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
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