Machine Learning Certification Programme
(All course fees are in USD)
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.
Offered in Partnership with
- 14 hours of online self-paced learning
- 44 hours of online instructor-led training
Total: 58 hours of online blended learning
- Total 58 hours online blended learning (44 hours online instructor-led vritual classes, and 14 hours online self-paced pre-recorded learning)
- Gain expertise with 25+ hands-on exercises
- 4 real-life industry projects
- Interactive learning with Jupyter notebooks integrated labs
- Dedicated mentoring sessions from industry experts
Skills to be Learned
Award upon Successful Completion
Machine Learning Certificate from Simplilearn
- 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
- 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.
- Attend one complete batch of online virtual classes
- Submit at least one completed project & pass.
- 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
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.
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
- California Housing Price Prediction
- Phishing Detector with LR
Access Period of Course
1 Year from date of enrolment
Machine Learning Engineer
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!
I like Simplilearn courses for the following reasons: 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.
- Students 0 student
- Max Students1000
- Duration58 hour
- Skill levelall
- Re-take course20000
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
Lesson 7 - Unsupervised Learning
Lesson 8 - Time Series Modeling
Lesson 9 - Ensemble Learning
Lesson 10 - Recommender System
Lesson 11 - Text Mining
Lesson 12 - Project Highlights
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