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  • 32 hours of instructor-led training
  • Gain expertise with 25+ hands-on exercises
  • Practical application of 15+ Machine Learning algorithms
  • Master the concepts of Supervised & Unsupervised Learning

Course description

  • Why learn Machine learning?

    • Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
    • The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period
    Why learn Machine Learning

  • What are the course objectives?

    A form of artificial intelligence, machine learning is revolutionizing the world of computing as well as all people’s digital interactions. By making it possible to quickly, cheaply and automatically process and analyze huge volumes of complex data, machine learning is critical to countless new and future applications. Machine learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.

    This Machine Learning online course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in machine learning. The demand for machine learning skills is growing quickly. The median salary of a Machine Learning Engineer is $134,293 (USD), according to payscale.com.
     

  • What skills will you learn with our Machine Learning Course?

    By the end of this Machine Learning course, you will be able to accomplish the following: 

    • Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
    • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
    • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
    • Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
    • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
    • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems

     

  • Who should take this Machine Learning Training Course?

    There is an increasing demand for skilled machine meaning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

    • Developers aspiring to be a data scientist or machine learning engineer
    • Analytics managers who are leading a team of analysts 
    • Business analysts who want to understand data science techniques
    • Information architects who want to gain expertise in machine learning algorithms 
    • Analytics professionals who want to work in machine learning or artificial intelligence
    • Graduates looking to build a career in data science and machine learning
    • Experienced professionals who would like to harness machine learning in their fields to get more insights

  • What projects are included in this Machine Learning Online Training Course?

    Simplilearn's Machine Learning Training course is very hands-on and code-driven. The theoretical motivation and Mathematical problem formulation must be provided only when introducing concepts.

    This course consists of one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms. 

    Capstone Project Details:
    Project Name:     Predicting house prices in California
    Description: The project involves building a model that predicts median house values in Californian districts.You will be given metrics such as population, median income, median housing price and so on for each block group in California.Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people).The model you build should learn from this data and be able to predict the median housing price in any district.
     

    Concept covered: Techniques of Machine Learning
    Case Study 1: Predict whether consumers will buy houses or not, from the given dataset,
    provided with their age and salary 
    Project 1: What issues do you see in the plot produced by the code in reference to the above problem statement?
    Project  2: What are the approximate prices of the houses with areas 1700 and 1900?
     
    Concept covered: Data Preprocessing
    Case Study 2: Demonstrate methods to handle missing data, categorical data, and data standardization using the information provided in the dataset
    Project 3: Review the training dataset (Excel file). Note that weight is missing for the fifth and eighth rows.What are the values computed by the imputer for these two missing rows?
    Project 4: In the tutorial code, find the call to the Imputer class. Replace strategy parameter from “mean” to “median” and execute it again. What is the new value assigned to the blank fields Weight and Height for the two rows?
    Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

    Case Study 3: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided
    Project 6: What does the hyperplane shadow represent in the PCA output chart on random data?
    Project 7: What is the reconstruction error after PCA transformation? Give interpretation.

    Concept Covered: Regression
    Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided
    Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and interpret the resulting regression plot. Specify if it is under fitted, right-fitted, or overfitted?
    Project 9: Predict the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.
    Project 10: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

    Case Study 5: Predict insurance premium per year based on a person’s age using Decision Trees using the information provided in the dataset
    Project 11: Modify the code to predict insurance claim values for anyone above the age of 55 in the given dataset.

    Case Study 6: Generate random quadratic data and demonstrate Decision Tree regression 
    Project 12: Modify the max_depth from 2 to 3 or 4, and observe the output.
    Project 13: Modify the max_depth to 20, and observe the output
    Project 14: What is the class prediction for petal_length = 3 cm and petal_width = 1 cm for the max_depth = 2?
    Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes exist in the two cases? What does average value represent these two situations? Use the information provided
    Project 16: Modify the regularization parameter min_sample_leaf from 10 to 6, and check the output of Decision Tree regression. What is the result and why?

    Case Study 7: Predict insurance per year based on a person’s age using Random Forests.
    Project 17: What is the output insurance value for individuals aged 60 and with n_estimators = 10?

    Case Study 8:  Demonstrate various regression techniques over a random dataset using the information provided in the dataset
    Project 18: The program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your interpretation of these charts?
    Project 19: The program depicts the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try changing the values to 0.001, 0.25, and 0.9 and check the results? Provide interpretation.
     
    Concept Covered: Classification
    Case Study 9: Predict if the consumers will buy houses, given their age and salary.  Use the information provided in the dataset
    Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Modify the code to change the value of nearest_neighbours to 2 and 20, and note the observations. 
     
    Case Study 10: Classify IRIS dataset using SVM, and demonstrate how Kernel SVMs can help classify non-linear data.
    Project 21: Modify the kernel trick from RBF to linear to see the type of classifier that is produced for the XOR data in this program. Interpret the data. 
    Project 22:  For the Iris dataset, add a new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Explain the output.
     
    Case Study 11: Classify IRIS flower dataset using Decision Trees. Use the information provided
    Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and show the tree output. 
    Project 24:  Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.

    Case Study 12: Classify the IRIS flower dataset using various classification algorithms. Use the information provided
    Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms?

    Concept Covered: Unsupervised Learning with Clustering
    Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.
    Project 26:  Modify the number of clusters k to 2, and note the observations.
    Project 27:  Modify the n_samples from 150 to 15000 and the number of centres to 4 with n_clusters as 3. Check the output, and note your observations.
    Project 28:  Modify the code to change the n_samples from 150 to 15000 and number of centres to 4, keeping n_clusters at 4. Check the output.
    Project 29: Modify the number of clusters k to 6, and note the observations.

Course preview

  • Machine Learning
    • Lesson 1: Introduction to Artificial Intelligence and Machine Learning 32:24
      • 1.01- Introduction to AI and Machine Learning32:24
    • Lesson 2: Techniques of Machine Learning 24:01
      • 2.01- Techniques of Machine Learning24:01
    • Lesson 3: Data Preprocessing 1:15:56
      • 3.01- Data Preprocessing1:15:56
    • Lesson 4: Math Refresher 30:40
      • 4.01- Math Refresher30:40
    • Lesson 5: Regression 55:25
      • 5.01- Regression55:25
    • Lesson 6: Classification 1:03:41
      • 6.01 Classification1:03:41
    • Lesson 7: Unsupervised learning - Clustering 13:05
      • 7.01- Unsupervised Learning with Clustering13:05
    • Lesson 8: Introduction to Deep Learning 10:03
      • 8.01- Introduction to Deep Learning10:03
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    Python Programming for Beginners
    • Section 1 - Getting Started with Python 20:58
      • 1.1 Getting Started with Python09:53
      • 1.2 Print and Strings08:11
      • 1.3 Math02:54
    • Section 2 - Variables, Loops and Statements 38:17
      • 2.1 Variables, Loops and Statements04:58
      • 2.2 While Loops06:13
      • 2.3 For Loops05:13
      • 2.4 If Statments06:59
      • 2.5 If Else Statements04:12
      • 2.6 If Elif Else Statements10:42
    • Section 3 - Functions and Variables 29:57
      • 3.1 Functions And Variables05:21
      • 3.2 Function Parameters15:00
      • 3.3 Global And Local Variables09:36
    • Section 4 - Understanding Error Detection 12:29
      • 4.1 Understanding Error Detection12:29
    • Section 5 - Working with Files and Classes 16:40
      • 5.1 Working With Files And Classes04:45
      • 5.2 Appending To A File03:29
      • 5.3 Reading From A File03:47
      • 5.4 Classes04:39
    • Section 6 - Intermediate Python 54:19
      • 6.1 Intermediate Python07:55
      • 6.2 Import Syntax06:53
      • 6.3 Making Modules06:39
      • 6.4 Error Handling - Try And Accept13:10
      • 6.5 Lists vs Tuples And List Manipulation11:03
      • 6.6 Dictionaries08:39
    • Section 7 - Conclusion 27:22
      • 7.1 Conclusion27:22
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    • Module 01 - Course Introduction 05:08
      • 1.1 Course Introduction04:10
      • 1.2 Overview of Final Project00:58
    • Module 02 - Introduction to Django 59:11
      • 2.1 Introduction00:35
      • 2.2 Django Installation And Configuration11:19
      • 2.3 MVC Applied To Django Plus Git08:19
      • 2.4 Basic Views, Templates And Urls15:37
      • 2.5 Models, Databases, Migrations and the Django Admin19:07
      • 2.6 Section Recap01:37
      • 2.7 Quiz02:37
    • Module 03 - Creating a User Authentication System 56:49
      • 3.1 What You Will Learn In This Section01:04
      • 3.2 Setting Up A Simple User Authentication System22:26
      • 3.3 Login and Session Variables18:40
      • 3.4 Social Registration13:29
      • 3.5 Review00:32
      • 3.6 Quiz00:38
    • Module 04 - Frontending 55:42
      • 4.1 What You Will Learn In This Section00:29
      • 4.2 Template Language and Static Files16:49
      • 4.3 Twitter Bootstrap Integration20:17
      • 4.4 Static File Compression And Template Refactoring17:05
      • 4.5 Review00:36
      • 4.6 Quiz00:26
    • Module 05 - E-Commerce 1:09:33
      • 5.1 What You Will Learn In This Section00:24
      • 5.2 Preparing The Storefront26:35
      • 5.3 Adding A Shopping Cart20:12
      • 5.4 Paypal Integration21:11
      • 5.5 Stripe Integration With Ajax00:01
      • 5.6 Review00:41
      • 5.7 Quiz00:29
    • Module 06 - File Uploading, Ajax and E-mailing 39:28
      • 6.1 What You Will Learn In This Section00:37
      • 6.2 File Upload14:04
      • 6.3 Forms13:19
      • 6.4 Advanced Emailing10:25
      • 6.5 Review00:38
      • 6.6 Quiz00:25
    • Module 07 - Geolocation and Map Integration 18:36
      • 7.1 What You Will Learn In This Section00:37
      • 7.2 Adding A Map Representation With Geolocation08:35
      • 7.3 Advanced Map Usage08:24
      • 7.4 Review00:31
      • 7.5 Quiz00:29
    • Module 08 - Django Power-Ups Services and Signals 20:11
      • 8.1 What You Will Learn In This Section00:52
      • 8.2 Building A Web Service With Tastypie11:04
      • 8.3 Signals08:15
    • Module 09 - Testing Your Site 36:20
      • 9.1 What You Will Learn In This Section00:21
      • 9.2 Adding The Django Debug Toolbar04:36
      • 9.3 Unit Testing18:05
      • 9.4 Logging12:14
      • 9.5 Review00:40
      • 9.6 Quiz00:24
    • Module 10 - Course Conclusion 04:55
      • 10.1 Conclusion04:55
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    • Python Game Development - Create a Flappy Bird Clone 2:57:17
      • 1.1 Introduction to the Course and the Game03:08
      • 1.2 Introduction to PyGame and Initial Coding09:04
      • 1.3 Time Clock and Game Over10:24
      • 1.4 Graphics Setup02:59
      • 1.5 Background and Adding Graphics to the Screen06:06
      • 1.6 Working with Coordinates06:02
      • 1.7 Creating Input Controls11:17
      • 1.8 Boundaries, Crash Events and Menu Creation09:47
      • 1.9 Part 209:37
      • 1.10 Part 306:56
      • 1.11 Part 407:58
      • 1.12 Creating Obstacles Using Polygons07:38
      • 1.13 Completing Our Obstacles09:08
      • 1.14 Game Logic Using Block Logic12:43
      • 1.15 Game Logic Success Or Failure12:19
      • 1.16 Hitting Obstacles Part 205:11
      • 1.17 Creating the Score Display12:00
      • 1.18 Adding Colors and Difficulty Levels12:27
      • 1.19 Adding Colors Part 212:53
      • 1.20 Adding Difficulty Levels09:40
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    Data Science with Python
    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science08:42
      • 1.2 Different Sectors Using Data Science05:59
      • 1.3 Purpose and Components of Python05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique00:57
      • 2.6 Data Analytics Conclusion or Predictions04:30
      • 2.7 Data Analytics Communication02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram03:59
      • 3.11 Knowledge Check
      • 3.12 Testing08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python13:28
      • 4.4 Basic Operators and Functions06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo 01-Creating and Printing an ndarray04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy05:01
      • 5.10 Assignment 01
      • 5.11 Assignment 01 Demo03:55
      • 5.12 Assignment 02
      • 5.13 Assignment 02 Demo03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy06:57
      • 6.2 SciPy Sub Package - Integration and Optimization05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO05:46
      • 6.8 Assignment 01
      • 6.9 Assignment 01 Demo01:20
      • 6.10 Assignment 02
      • 6.11 Assignment 02 Demo00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame05:31
      • 7.4 View and Select Data Demo05:34
      • 7.5 Missing Values03:16
      • 7.6 Data Operations09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation02:00
      • 7.11 Assignment 01
      • 7.12 Assignment 01 Demo04:09
      • 7.13 Assignment 02
      • 7.14 Assignment 02 Demo02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach03:57
      • 8.2 Steps 1 and 201:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works01:24
      • 8.5 Steps 5 and 601:54
      • 8.6 Supervised Learning Model Considerations00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression11:19
      • 8.11 Supervised Learning Models - Logistic Regression08:43
      • 8.12 Unsupervised Learning Models10:40
      • 8.13 Pipeline02:37
      • 8.14 Model Persistence and Evaluation05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Assignment 0105:45
      • 8.18 Assignment 02
      • 8.19 Assignment 0205:14
      • 8.20 Quiz
      • 8.21 Key Takeaways01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search10:17
      • 9.7 Assignment 01
      • 9.8 Demo Assignment 0106:32
      • 9.9 Assignment 02
      • 9.10 Demo Assignment 0208:00
      • 9.11 Quiz
      • 9.12 Key Takeaway01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots09:34
      • 10.7 Assignment 01
      • 10.8 Assignment 01 Demo02:23
      • 10.9 Assignment 02
      • 10.10 Assignment 02 Demo01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree12:56
      • 11.4 Navigating options
      • 11.5 Demo3 Navigating a Tree04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree05:38
      • 11.8 Parsing and Printing the Document09:05
      • 11.9 Assignment 01
      • 11.10 Assignment 01 Demo01:55
      • 11.11 Assignment 02
      • 11.12 Assignment 02 demo04:57
      • 11.13 Quiz
      • 11.14 Key takeaways00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming07:20
      • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark07:43
      • 12.7 Demo 02 - Using PySpark to Determine Word Count04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Assignment 01 Demo02:47
      • 12.11 Assignment 02
      • 12.12 Assignment 02 Demo03:30
      • 12.13 Quiz
      • 12.14 Key takeaways01:20
    • Project 1 18:36
      • Project 1 Stock Market Data Analysis18:36
    • Project 2 20:06
      • Project 02
      • Main project 0220:06
    • Course Feedback
      • Course Feedback
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Exam & certification

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:
    • Attend one complete batch.
    • Complete 1 project.
    Online Self-Learning:
    • Complete 85% of the course.
    • Complete 1 project.

  • What are the prerequisites for this Machine Learning course?

    Participants in this Machine Learning online course should have:

    • Familiarity with the fundamentals of Python programming 
    • Basic high school mathematics
    • Understanding of the basics of statistics

    The course covers concepts of mathematics & statistics required for machine learning and we will provide you with a free Python course when you purchase our Machine Learning course. 

Reviews

Parichay Bose
Parichay Bose Solutions Architect at Ericsson, Mississauga

I have been taking multiple courses from Simplilearn including Big Data Hadoop, Machine Learning, MEAN Stack. Apart from awesome content and trainer, they have amazing support executive that makes me feel cared. The customer support is helpful and is always there whenever you need help. That is where other online training programs are lagging behind. Well done Simplilearn!

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Vijay Marupadi
Vijay Marupadi Project Manager at Canadas Best Store Fixtures, Mississauga

The Simplilearn learning experience was beyond my expectation. The professionalism with which the training was carried out is worth commending. I would readily recommend Simplilearn to anyone who wants to pursue a career through online learning. It's worth the money. Happy learning with Simplilearn!

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Leela Krishna
Leela Krishna Senior Operations Professional at IBM INDIA PVT. LTD., Bangalore

The course was very informative. The study material provided by the trainer was extremely helpful and very easy to understand.

Rajendra Kumar Rana
Rajendra Kumar Rana Senior Software Engineer RPA at Tech Mahindra, Pune

The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.

Anuvrat Kulkarni
Anuvrat Kulkarni Development Analyst in Social Media at Accenture, Bangalore

My experience with Simplilearn has been very enriching. The faculty was quite experienced and had a deep knowledge of the subject. I am happy with Simplilearn and would definitely recommend others.

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Course advisor

Vivek Singhal
Vivek Singhal Co-Founder & Chief Data Scientist, CellStrat

Vivek is an entrepreneur and thought Leader in Artificial Intelligence and deep-tech industries. He is a leading data scientist and researcher with expertise in AI, Machine Learning, and Deep Learning. 

 

FAQs

  • Who are the instructors and how are they selected?

    All of our highly qualified trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.
     

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administrative fee. To learn more, please read our Refund Policy.
     

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide more details.

  • How do I enroll for the online training?

    You can enroll for this training on our website and make an online payment using any of the following options:
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • Who can I contact to learn more about this Machine Learning course?

    Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
     

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat and telephone. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
     

  • What if I miss a class?

    Simplilearn has Flexi-pass that lets you attend classes to blend in with your busy schedule and gives you an advantage of being trained by world-class faculty with decades of industry experience combining the best of online classroom training and self-paced learning
    With Flexi-pass, Simplilearn gives you access to as many as 15 sessions for 90 days.

  • What is CloudLab?

    CloudLab is a cloud-based Python environment lab that Simplilearn offers with the Machine Learning course to ensure a hassle-free execution of your hands-on projects. There is no need to install and maintain Python and it’s libraries on a virtual machine. Instead, you’ll be able to access a pre configured environment on CloudLab via your browser.


    You will have access to Simplilearn’s online CloudLab platform, from the Simplilearn Learning Management System (LMS) for the duration of the course.

  • How will I execute projects in this Machine Learning training course?

    You will use Simplilearn’s CloudLab to complete projects.

  • I am not able to access the online course. Who can help me?

    Contact us using the form on the right of any page on the Simplilearn website, select the Live Chat link or contact Help & Support.

  • Do you provide a money back guarantee for the training programs?

    Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

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    Contact Us

    +1-844-532-7688

    (Toll Free)

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    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
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