Start Date November 10, 2021
More Start Dates
The program length for day and evening classes may vary based on the number of scheduled hours per week. Holidays, school closings, and internship schedules may cause the program completion dates to vary as well.
1 Month
The standard classroom schedule is provided here. The clinical internship schedule will vary based on the clinical site. Students must follow their assigned internship schedule, not the AIMS Education class schedule, academic calendar, or the observed holidays and breaks.
Monday - Friday
Cost $749
Delivery Method Online
Financial Assistance
Other financial aid options may be available for this program (scholarships, training grants, VA benefits, etc.). Please contact our financial aid department to learn more.
Course Overview: 

The Machine Learning program helps you master the concepts of machine learning, data preprocessing, supervised and unsupervised learning, ensemble learning, regression, classification,  recommendation engines, and time-series modeling. You will also learn how to implement machine learning models and use Python to draw predictions from data.

Course Highlights:
  • 58 hours of applied learning
  • Four industry-based course-end projects
  • Interactive learning with Jupyter notebooks and integrated labs
  • Dedicated mentoring session from industry experts
Course Delivery Method: 

Online Bootcamp– Online self-paced Video-based learning and live virtual classroom conducted by Industry’s leading coach. This course includes Simpliearn’s Integrated lab platform.


For taking this Machine Learning program, you should have a basic understanding of statistics, mathematics, and python programming.

Skills Covered: 
  • Data preprocessing
  • Supervised and unsupervised learning
  • Time-series modeling
  • Ensemble learning
  • Regression
  • k-means clustering
  • Text mining
Learning Path
  • Lesson 1

    Course Introduction

  • Lesson 2

    Introduction to AI and Machine Learning

  • Lesson 3

    Data Preprocessing

  • Lesson 4

    Supervised Learning

  • Lesson 5

    Feature Engineering

  • Lesson 6

    Supervised Learning Classification

View more

  • Lesson 7

    Unsupervised Learning

  • Lesson 8

    Time Series Modeling

  • Lesson 9

    Ensemble Learning

  • Lesson 10

    Recommender Systems

  • Lesson 11

    Text Mining

  • Lesson 12

    Project Highlights

  • Lesson 13

    Stream Processing Frameworks and Spark Streaming

  • Lesson 14

    Spark GraphX

Who Will Benefit: 

The Machine Learning program is best suited for analytics managers, business analysts, information architects,  data scientists, and graduates looking for a career in the data science and machine learning field.

Key Learning Outcomes: 
  • Master the concepts of supervised and unsupervised learning, recommendation engines, and time-series modeling
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach 
  • Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
  • Implement models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, k-means clustering, and more in Python
  • Validate machine learning models and decode various accuracy metrics
  • Improve the final models using another set of optimization algorithms, which include boosting  and bagging techniques
  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning
Certification Criteria:
  • At least 85 percent attendance of one live virtual classroom 
  • A score of at least 75 percent in course-end assessment 
  • Successful evaluation in the course-end project

Career Outlook

Expected Growth (2019 – 2029)*
  • 15%
Annual Average US Salary*
  • $92,00 - $140,000
Demanding Fields
  • Informaon Technology
  • Finance
  • Retail
  • Real Estate
  • Engineering
  • Hospitality Management
  • Business Consulng
Job Opportunies for Professionals
  • IT Developers
  • Analytics Managers
  • Information Architects
  • Analytics professionals
  • Experienced professionals
  • Beginners or Recent
          Graduates in Bachelors or
          Master’s Degree

*Salary and job outlook information comes from the US Bureau of Labor Statistics and Projections Central. Employment outcomes are not guaranteed.