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Course description

  • Why learn Machine learning?

    • Machine learning is taking over the world- and with that, there is a growing need among companies in Melbourne for professionals to know the inputs and outputs 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 objectives of our Machine Learning Certification Training course?

    This Machine Learning online course will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including retail, e-commerce, automotive, social media and more. Your problem solving across these domains will hone your skills developing machine learning algorithms such as regression, classification, time series modelling and much more.

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

    • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
    • 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 & Bagging techniques
    • Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

  • Who can take this Machine Learning Training Course?

    There is an increasing demand for skilled machine meaning engineers across all industries in Australia. Simplilearn made this Machine Learning certification course in such a way this is 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 course is hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms. 

    Project 1: Fare Prediction for Uber

    Domain: Delivery (Commerce)
    Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.

    Project 2: Test bench time reduction for Mercedes-Benz

    Domain: Automobile

    Mercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimise the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective. 

    Project 3: Income qualification prediction for Inter-American Development bank

    Many social programs have a hard time making sure the right people are given enough aid.
    It’s tricky when a program focuses on the poorest segment of the population. This segment of
    population can’t provide the necessary income and expense records to prove that they qualify.
    Predicting the right set of people to be included for the aid remains a big challenge for  Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using random forest classifier.

    Project 4: Access privileges prediction for Amazon.com employees

    There is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their
    provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.


     

  • 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 
    • Fair understanding of the basics of statistics and mathematics

Course preview

  • Machine Learning
    • Lesson 1: Introduction to Artificial Intelligence and Machine Learning 32:24
      • 1.01- Introduction to AI and Machine Learning 32:24
    • Lesson 2: Techniques of Machine Learning 24:01
      • 2.01- Techniques of Machine Learning 24:01
    • Lesson 3: Data Preprocessing 1:15:56
      • 3.01- Data Preprocessing 1:15:56
    • Lesson 4: Math Refresher 30:40
      • 4.01- Math Refresher 30:40
    • Lesson 5: Regression 55:25
      • 5.01- Regression 55:25
    • Lesson 6: Classification 1:03:41
      • 6.01 Classification 1:03:41
    • Lesson 7: Unsupervised learning - Clustering 13:05
      • 7.01- Unsupervised Learning with Clustering 13:05
    • Lesson 8: Introduction to Deep Learning 10:03
      • 8.01- Introduction to Deep Learning 10:03
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    • Section 1 - Getting Started with Python 20:58
      • 1.1 Getting Started with Python 09:53
      • 1.2 Print and Strings 08:11
      • 1.3 Math 02:54
    • Section 2 - Variables, Loops and Statements 38:17
      • 2.1 Variables, Loops and Statements 04:58
      • 2.2 While Loops 06:13
      • 2.3 For Loops 05:13
      • 2.4 If Statments 06:59
      • 2.5 If Else Statements 04:12
      • 2.6 If Elif Else Statements 10:42
    • Section 3 - Functions and Variables 29:57
      • 3.1 Functions And Variables 05:21
      • 3.2 Function Parameters 15:00
      • 3.3 Global And Local Variables 09:36
    • Section 4 - Understanding Error Detection 12:29
      • 4.1 Understanding Error Detection 12:29
    • Section 5 - Working with Files and Classes 16:40
      • 5.1 Working With Files And Classes 04:45
      • 5.2 Appending To A File 03:29
      • 5.3 Reading From A File 03:47
      • 5.4 Classes 04:39
    • Section 6 - Intermediate Python 54:19
      • 6.1 Intermediate Python 07:55
      • 6.2 Import Syntax 06:53
      • 6.3 Making Modules 06:39
      • 6.4 Error Handling - Try And Accept 13:10
      • 6.5 Lists vs Tuples And List Manipulation 11:03
      • 6.6 Dictionaries 08:39
    • Section 7 - Conclusion 27:22
      • 7.1 Conclusion 27:22
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    • Module 01 - Course Introduction 05:08
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    • Module 02 - Introduction to Django 59:11
      • 2.1 Introduction 00:35
      • 2.2 Django Installation And Configuration 11:19
      • 2.3 MVC Applied To Django Plus Git 08:19
      • 2.4 Basic Views, Templates And Urls 15:37
      • 2.5 Models, Databases, Migrations and the Django Admin 19:07
      • 2.6 Section Recap 01:37
      • 2.7 Quiz 02:37
    • Module 03 - Creating a User Authentication System 56:49
      • 3.1 What You Will Learn In This Section 01:04
      • 3.2 Setting Up A Simple User Authentication System 22:26
      • 3.3 Login and Session Variables 18:40
      • 3.4 Social Registration 13:29
      • 3.5 Review 00:32
      • 3.6 Quiz 00:38
    • Module 04 - Frontending 55:42
      • 4.1 What You Will Learn In This Section 00:29
      • 4.2 Template Language and Static Files 16:49
      • 4.3 Twitter Bootstrap Integration 20:17
      • 4.4 Static File Compression And Template Refactoring 17:05
      • 4.5 Review 00:36
      • 4.6 Quiz 00:26
    • Module 05 - E-Commerce 1:30:03
      • 5.1 What You Will Learn In This Section 00:24
      • 5.2 Preparing The Storefront 26:35
      • 5.3 Adding A Shopping Cart 20:12
      • 5.4 Paypal Integration 21:11
      • 5.5 Stripe Integration With Ajax 20:31
      • 5.6 Review 00:41
      • 5.7 Quiz 00:29
    • Module 06 - File Uploading, Ajax and E-mailing 39:28
      • 6.1 What You Will Learn In This Section 00:37
      • 6.2 File Upload 14:04
      • 6.3 Forms 13:19
      • 6.4 Advanced Emailing 10:25
      • 6.5 Review 00:38
      • 6.6 Quiz 00:25
    • Module 07 - Geolocation and Map Integration 18:36
      • 7.1 What You Will Learn In This Section 00:37
      • 7.2 Adding A Map Representation With Geolocation 08:35
      • 7.3 Advanced Map Usage 08:24
      • 7.4 Review 00:31
      • 7.5 Quiz 00:29
    • Module 08 - Django Power-Ups Services and Signals 20:11
      • 8.1 What You Will Learn In This Section 00:52
      • 8.2 Building A Web Service With Tastypie 11:04
      • 8.3 Signals 08:15
    • Module 09 - Testing Your Site 36:20
      • 9.1 What You Will Learn In This Section 00:21
      • 9.2 Adding The Django Debug Toolbar 04:36
      • 9.3 Unit Testing 18:05
      • 9.4 Logging 12:14
      • 9.5 Review 00:40
      • 9.6 Quiz 00:24
    • Module 10 - Course Conclusion 04:55
      • 10.1 Conclusion 04:55
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    • Python Game Development - Create a Flappy Bird Clone 2:57:17
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      • 1.2 Introduction to PyGame and Initial Coding 09:04
      • 1.3 Time Clock and Game Over 10:24
      • 1.4 Graphics Setup 02:59
      • 1.5 Background and Adding Graphics to the Screen 06:06
      • 1.6 Working with Coordinates 06:02
      • 1.7 Creating Input Controls 11:17
      • 1.8 Boundaries, Crash Events and Menu Creation 09:47
      • 1.9 Part 2 09:37
      • 1.10 Part 3 06:56
      • 1.11 Part 4 07:58
      • 1.12 Creating Obstacles Using Polygons 07:38
      • 1.13 Completing Our Obstacles 09:08
      • 1.14 Game Logic Using Block Logic 12:43
      • 1.15 Game Logic Success Or Failure 12:19
      • 1.16 Hitting Obstacles Part 2 05:11
      • 1.17 Creating the Score Display 12:00
      • 1.18 Adding Colors and Difficulty Levels 12:27
      • 1.19 Adding Colors Part 2 12:53
      • 1.20 Adding Difficulty Levels 09:40
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    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science 08:42
      • 1.2 Different Sectors Using Data Science 05:59
      • 1.3 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique 00:57
      • 2.6 Data Analytics Conclusion or Predictions 04:30
      • 2.7 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.12 Testing 08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python 13:28
      • 4.4 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo -Creating and Printing an nd array 04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations 07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy 05:01
      • 5.10 Assignment 01
      • 5.11 Demo - Assignment 01 03:55
      • 5.12 Assignment 02
      • 5.13 Demo - Assignment 02 03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy 06:57
      • 6.2 SciPy Sub Package - Integration and Optimization 05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector 01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO 05:46
      • 6.8 Assignment 01
      • 6.9 Demo - Assignment 01 01:20
      • 6.10 Assignment 02
      • 6.11 Demo - Assignment 02 00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas 12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame 05:31
      • 7.4 View and Select Data Demo 05:34
      • 7.5 Missing Values 03:16
      • 7.6 Data Operations 09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support 00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation 02:00
      • 7.11 Assignment 01
      • 7.12 Demo - Assignment 01 04:09
      • 7.13 Assignment 02
      • 7.14 Demo - Assignment 02 02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach 03:57
      • 8.2 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works 01:24
      • 8.5 Steps 5 and 6 01:54
      • 8.6 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression 11:19
      • 8.11 Supervised Learning Models - Logistic Regression 08:43
      • 8.12 Unsupervised Learning Models 10:40
      • 8.13 Pipeline 02:37
      • 8.14 Model Persistence and Evaluation 05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Demo - Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.19 Demo - Assignment 02 05:14
      • 8.20 Quiz
      • 8.21 Key Takeaways 01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview 10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit 12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search 10:17
      • 9.7 Assignment 01
      • 9.8 Demo - Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.10 Demo - Assignment 02 08:00
      • 9.11 Quiz
      • 9.12 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization 08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots 10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots 09:34
      • 10.7 Assignment 01
      • 10.8 Demo - Assignment 01 02:23
      • 10.9 Assignment 02
      • 10.10 Demo - Assignment 02 01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing 12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree 12:56
      • 11.4 Navigating options
      • 11.5 Demo - Navigating a Tree 04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree 05:38
      • 11.8 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.10 Demo - Assignment 01 01:55
      • 11.11 Assignment 02
      • 11.12 Demo - Assignment 02 04:57
      • 11.13 Quiz
      • 11.14 Key takeaways 00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python 04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming 07:20
      • 12.4 Demo - Using Hadoop Streaming for Calculating Word Count 08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark 07:43
      • 12.7 Demo - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Demo - Assignment 01 02:47
      • 12.11 Assignment 02
      • 12.12 Demo - Assignment 02 03:30
      • 12.13 Quiz
      • 12.14 Key takeaways 01:20
  • Free Course
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Exam & certification

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

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

    Online Classroom:

    • Attend one complete batch
    • Submit at least one completed project.

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project.
       

Course advisor

Mike Tamir
Mike Tamir No. 1 AI & Machine Learning Influencer, Head of Data Science - Uber ATG

​Named by Onalytica as the No.1 influencer in AI & Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

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. 

 

Reviews

Somil Gadhwal
Somil Gadhwal Application Engineer, Hyderabad

Simplilearn's course content is designed in a way that every session is closely connected to the next. There is no need to mug up the lessons. The instructors put thought into training and motivating students. I am really happy I joined the course.

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Afrid Mondal
Afrid Mondal Nagpur

The training was fantastic. Thank you for providing a great platform to learn.

Roveena Sebastian
Roveena Sebastian R & D Manager, Bangalore

It was a great learning experience. The hands-on assignments and the various resources provided by Simplilearn is excellent. The live sessions were simply awesome. The trainer was so successful in keeping the remote class active, covered all topics in such a short span and majorly ensured that the entire class was moving forward together in spite of the known time constraints. My special thanks to the trainer for his interest and dedication. Thanks once again to Simplilearn.

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Deboleena Paul
Deboleena Paul Solution Architect, Meerut

I really liked the trainer. He is very patient, well organized, and interactive. I had an awesome learning experience with Simplilearn that was beyond my expectation for an online classroom.

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Happy Snehal
Happy Snehal Data Science Intern at ABB, Bangalore

My experience with Simplilearn has been amazing. This is the fifth course that I have done from here and all the courses provide the best quality knowledge. Machine Learning course content was wide and deep. It covered algorithms, Python programming, Mathematics, and Statistics. It also provided project support. My customer support experience has been fantastic as within seconds or minutes, I have been provided with solutions and all my issues have been resolved to my full satisfaction. The faculties are well educated, well experienced, humble, kind and eager to teach things.

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Aditi Dalal
Aditi Dalal Analyst (Data Analytics) at The Smart Cube, Noida

I have enrolled in Machine Learning from Simplilearn. The content of the course is elaborate and easy to understand. The faculty has clarity in his way of explaining, maintains a very good balance between theory and the practical process. It has been a great learning experience for me.

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Siddhant Vibhute
Siddhant Vibhute M.Tech Scholar at VJTI, Mumbai

Simplilearn provides a platform to explore the subject in depth. The way it connects every problem with the real world makes the subject even more interesting. The trainers and support staff act promptly to each query with every possible help. Machine Learning course is definitely one of my best experiences and is highly recommended for every data scientist aspirant.

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Ujjwal Seth
Ujjwal Seth Data Analyst at Hewlett Packard Enterprise, Chennai

I have completed Machine Learning certification recently from Simplilearn. It felt amused how I was able to this skill when I was finding it super-hard when learning through some of the other online platforms. No Doubt that I feel Simplilearn is the Best Online Platform for learning Computer Science Skills! The Online Lab access gives complete tech resources using which you can execute Computer code and don't need to install the software on your laptop. The whole system is both simplistic and 1uality wise absolutely to the point and makes the user experience simple and beautiful. The content of the course was interesting and it used a lot of real-life application which helped me to understand better. The customer support was very supportive and always ready to help us. In fact, they always assured that our problem will be solved and the response was quick. Hence, A curious mind should not miss a chance to enroll in his preferred course at Simplilearn.

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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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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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Deboleena Paul
Deboleena Paul Senior Technical Lead at HCL Technologies, Lucknow

My experience while doing machine learning certification from Simplilearnwas was beyond my expectation for an online classroom. The trainer was great. He was very patient and interactive.

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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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FAQs

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.
     
     

  • 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.  

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

  • What is Machine Learning?

    Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.
     
     

  • Is this live training, or will I watch pre-recorded videos?

    If you enrol for self-paced e-learning, you will have access to pre-recorded videos. If you enrol for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.

  • 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 online classroom training?

    Online classroom training for Machine Learning Certification is conducted via online live streaming of each class. The classes are conducted by a Machine Learning certified trainer with more than 15 years of work and training experience.
     

  • 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.
     

  • 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.

     

  • How do I enrol for this online training?

    You can enrol 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.

      

  • 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.

  • 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.

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    Contact Us

    1-800-982-536

    (Toll Free)

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    Our Melbourne Correspondence / Mailing address

    Simplilearn Americas, Inc, Level 28, 303 Collins St, Melbourne, 3000, Australia, Call us at:1-800-982-536

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    • Disclaimer
    • 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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