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  • Berkeley Global

Introduction to Machine Learning Using Python

COMPSCI X433.6

30544037
Delivery Options Online
Learn why the open-source programming language Python has been extensively adopted by the machine-learning community and industry. Python allows its users to create products that parse, reduce, simplify and categorize data, and then extract actionable intelligence from that data. In this course, you’ll use Python to understand machine-learning concepts, terms and methodology, and then build applications to gain an intuitive understanding of the mathematics underlying the program. Example real-world applications include search engines, image analysis, bioinformatics, industrial automation, speech recognition and more.

Course Outline

Expand or collapse section

Course Objectives

  • Identify and formulate machine-learning problems.
  • Understand and implement algorithms in Python to solve simple machine-learning problems.
  • Analyze the performance of given or implemented machine-learning solutions on practical datasets.

What You Learn

  • Machine learning and data mining
  • Histograms classifiers
  • Probability density functions
  • Bayesian classifiers
  • Class-conditional density, priors and posteriors
  • Python packages
  • Multidimensional data
  • Covariance matrix
  • Principal component analysis
  • Elements of higher-dimensional visualization
  • Linear classifiers
  • Perceptron, Fisher's linear discriminant, least-mean-square
  • Classifier performance evaluation
  • Accuracy, sensitivity, specificity, positive predictive value
  • Receiver operating characteristic
  • Training, testing and validation
  • Multinormal probability density function
  • Weighted means and covariance matrix
  • Mixture distributions
  • Unsupervised learning
  • Expectation maximization algorithm
  • K-means algorithm, k-nearest neighbor algorithm
  • Markov models
  • Dynamic programming
  • Viterbi algorithm
  • Ensemble learning
  • Outlier and anomaly detection
  • Neural networks
  • Activation functions
  • Backpropagation algorithm
  • Auto-encoder

How You Learn

  • Lecture and presentation of main topics
  • Worked examples that illustrate concepts
  • In-class quiz
  • Team activities
  • Group project
  • Online discussion and forum

Is This Course Right for You?

This course is geared to professionals, students and job-seekers interested in learning the fundamentals of machine learning and data mining and who want to build, evaluate or showcase machine learning applications in Python. Also, anyone interested in understanding the foundational mathematical concepts underlying modern data science should enroll.

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Sections

Section 013

Mar 04, 2021 to May 06, 2021 Live Online

Course Fee(s)

Course Fee credit (2 units)

$995.00


Type Live Online

Access classroom-style interactive learning from anywhere in the world! Attend scheduled online sessions with your instructor and classmates in addition to completing your coursework.

Live Online format allows you to take classes from anywhere with an internet connection. Classroom sections will be taught in this format through Spring 2021. Learn more about this format.

Beginning August 15, 2020, you must have a Zoom account to participate.

Many schools are now accepting transfer credit for online coursework, including health and sciences programs. Check with your institution before enrolling.

Days

Th

Time

6:00PM to 9:00PM Pacific Time

Dates

Mar 04, 2021 to May 06, 2021

Schedule and Location

View Details

Instructional Hours

30.0

Delivery Options

Online

Available for Credit

2 semester units

Instructors

  • Sridevi Pudipeddi

Section Notes

There is no meeting on Sep. 7, 2020 (Labor Day).

This course applies to the following programs:

Certificate Program in Data Science

Expand or collapse section

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

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 such as Python, Java, Ruby, JavaScript, C or C++. Need to fulfill this prereq? Take a course in:
    • Introduction to Computers and Programming COMPSCI X444.4
    • Programming Python COMPSCI X434
    • First Course in Java EL ENG X429.9
    • JavaScript and jQuery: An Introduction COMPSCI X452.1
    • Introduction to C Language Programming EL ENG X24
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