Expert Programmes

Machine Learning Certification Programme

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$999.00 $849.00
shutterstock_1568874958 R -180%
(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.

 

Offered in Partnership with
Simplilearn

 

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 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

     

    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

     

    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
    Practice Projects
    • California Housing Price Prediction
    • Phishing Detector with LR

     

    Access Period of Course

    1 Year from date of enrolment

     

    Customer Reviews
    Arjun Nemical

    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.

     

    Sharath Chenjeri

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

     

    Kalpesh Mahajan

    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.

     

     

    Course Features

    • Students 0 student
    • Max Students1000
    • Duration58 hour
    • Skill levelall
    • LanguageEnglish
    • Re-take course20000
    • Lesson 1 - Course Introduction
    • Lesson 2 - Introduction to AI and Machine Learning
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    • Lesson 3 - Data Preprocessing
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    • Lesson 4 - Supervised Learning
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    • Lesson 5 - Feature Engineering
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    • Lesson 6 - Supervised Learning Classification
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    • Lesson 7 - Unsupervised Learning
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    • Lesson 8 - Time Series Modeling
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    • Lesson 9 - Ensemble Learning
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    • Lesson 10 - Recommender System
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    • Lesson 11 - Text Mining
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    • Lesson 12 - Project Highlights
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