(All course fees are in USD)

Course Description

This Machine Learning online course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning certification training to draw predictions from data.

Course Delivery

• 14 hours of online self-paced learning
• 44 hours of online instructor-led training

Total: 58 hours of online blended learning

Benefits

• Total 58 hours online blended learning (44 hours online instructor-led virtual classes, and 14 hours online self-paced pre-recorded learning)
• 4 real-life industry projects
• Virtual lab with Jupyter notebooks included in the online course (1 year access)
• Dedicated mentoring sessions from industry experts

Skills to be Learned

Award upon Successful Completion

Machine Learning Certificate from Simplilearn

Awarding Organisation

Simplilearn

Learning Outcomes

• Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
• Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
• 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

Assessments

• Project 1: Uber Fare Prediction

Design an algorithm that will tell the fare to be charged for a passenger.

Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies for building its next-generation model.

• Project 2: Mercedes-Benz Greener Manufacturing

Reduce the time a Mercedes-Benz spends on the test bench.

Mercedes-Benz wants to shorten the time models spend on its test-bench, thus moving it to the marketing phase sooner. Build and optimize a machine learning algorithm to solve this problem.

• Project 3: Amazon.com – Employee Access

Design an algorithm to accurately predict access privileges for Amazon employees

Use the data of Amazon employees and their access permissions to build a model that automatically decides access privileges as employees enter and leave roles within Amazon

• Project 4: Income Qualification

The Inter-American Development bank wants to qualify people for an aid program.

Help the bank to build and improve the accuracy of the data set using a random forest classifier.

Certification Criteria

Online Classroom

• Attend one complete batch of online virtual classes
• Submit at least one completed project & pass.

Online Self-Learning

• Complete 85% of the course
• Submit at least one completed project.
• A score of at least 75 percent in the course-end assessment

Who Should Enrol

• Data analysts looking to upskill
• Data scientists engaged in prediction modeling
• Any professional with Python knowledge and interest in statistics and math
• Business intelligence developers
• Anyone interested in machine learning

Prerequisites

This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

Course Overview

Lesson 01 – Course Introduction

Lesson 02 – Introduction to AI and Machine Learning

Lesson 03 – Data Preprocessing

Lesson 04 – Supervised Learning

Lesson 05 – Feature Engineering

Lesson 06 – Supervised Learning: Classification

Lesson 07 – Unsupervised Learning

Lesson 08 – Time Series Modeling

Lesson 09 – Ensemble Learning

Lesson 10 – Recommender Systems

Lesson 11 – Text Mining

Access Period of Course

1 year from date of enrolment

Customer Reviews

The training was awesome. The instructor has done a great job. He was very patient throughout the sessions and took additional time to explain the concepts further when we had queries.

My trainer Sonal is amazing and very knowledgeable. The course content is well-planned, comprehensive, and elaborate. Thank you, Simplilearn!

……1.It provides a unique blend of theoretical and practical based approach. 2. The learning pace is comfortable. 3. They have global industry experts as trainers.

*Note: We reserve the right to revise/change any of the course content &/or instructor at our sole & absolute discretion, without prior notice to learner.