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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 e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of 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
    • Practice Projects
      • Uber Fare Prediction
      • Amazon.com Employee Access
      • Phishing detector with KNN
      • MNIST Classifier
  • Free Course
    Data Science with Python
    • Lesson 00 - Course Overview 04:34
      • 0.001 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.001 Introduction to Data Science 08:42
      • 1.002 Different Sectors Using Data Science 05:59
      • 1.003 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.001 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique 00:57
      • 2.006 Data Analytics Conclusion or Predictions 04:30
      • 2.007 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.012 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.001 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.012 Testing 08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.001 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python 13:28
      • 4.004 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.001 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray 04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations 07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy 05:01
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo 03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo 03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.001 Introduction to SciPy 06:57
      • 6.002 SciPy Sub Package - Integration and Optimization 05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector 01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO 05:46
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo 01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo 00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.001 Introduction to Pandas 12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame 05:31
      • 7.004 View and Select Data Demo 05:34
      • 7.005 Missing Values 03:16
      • 7.006 Data Operations 09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support 00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation 02:00
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo 04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo 02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.001 Machine Learning Approach 03:57
      • 8.002 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works 01:24
      • 8.005 Steps 5 and 6 01:54
      • 8.006 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression 11:19
      • 8.011 Supervised Learning Models - Logistic Regression 08:43
      • 8.012 Unsupervised Learning Models 10:40
      • 8.013 Pipeline 02:37
      • 8.014 Model Persistence and Evaluation 05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.017 Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02 05:14
      • 8.20 Quiz
      • 8.021 Key Takeaways 01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.001 NLP Overview 10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.004 NLP Libraries-Scikit 12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search 10:17
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02 08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.001 Introduction to Data Visualization 08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots 10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots 09:34
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo 02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo 01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.001 Web Scraping and Parsing 12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree 12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree 04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree 05:38
      • 11.008 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo 01:55
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo 04:57
      • 11.13 Quiz
      • 11.014 Key takeaways 00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.001 Why Big Data Solutions are Provided for Python 04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming 07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count 08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark 07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo 02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo 03:30
      • 12.13 Quiz
      • 12.014 Key takeaways 01:20
  • Free Course
    Programming with Python 3.X
    • Getting Started with Python 20:04
      • Installation 09:31
      • Print and Strings 07:47
      • Math 02:46
    • Variables, Loops and Statements 36:54
      • Variables 04:49
      • While Loops 06:00
      • For Loops 05:00
      • If Statements 06:43
      • If Else Statements 04:01
      • If Elif Else Statements 10:21
    • Functions and Global and Local Variables 28:20
      • Functions 05:03
      • Function Parameters 14:04
      • Global and Local Variables 09:13
    • Understanding Error Detection 11:35
      • Common Python Errors 11:35
    • Working with Files and Classes 15:49
      • Writing to a File 04:29
      • Appending to a File 03:23
      • Reading From a File 03:34
      • Classes 04:23
    • Intermediate Python 39:09
      • Input and Statistics 07:22
      • Import Syntax 06:39
      • Making Modules 06:20
      • Lists vs Tuples and List Manipulation 10:34
      • Dictionaries 08:14
    • Project 26:15
      • Problem Statement
      • Solution 26:15
  • Free Course
    Math Refresher
    • Math Refresher 30:36
      • Math Refresher 30:36
  • Free Course
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Exam & certification FREE PRACTICE TEST

  • Who provides the certificate and how long is it valid for?

    Upon successful completion of this course, Simplilearn will provide you with an industry-recognized course completion certificate which has a lifelong validity.

  • How do I become a Machine Learning expert?

    This course will give you a complete overview of  Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning expert. You will earn Simplilearn’s  Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.  

     


     

  • 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

Mahesh Gaonkar
Mahesh Gaonkar Software Engineer, Bangalore

Simplilearn is a great start for the beginner as well for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my Machine Learning advance course very easily with good project exercise.

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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 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.
     

  • 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.
     
      

  • 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 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.

  • 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.
     

  • How do I enroll in this online training?

    You can enroll in 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.

      

  • 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.
     

  • 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.

  • * Disclaimer

    * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

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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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