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Course description

  • Why Learn Machine Learning Certification?

     

    Machine Learning is the technology whose name can be now heard from all the corners of the world. The requirement for proficiency in this promising technology has never been so high.

    It is estimated that the market size of machine learning will increase 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?

     

    Machine Learning takes Artificial Intelligence to the next level. It has changed the way how people look at data computing and digital transformations. Several innovative applications that will launch in the future are backed by machine learning technology. It is due to the fact that machine learning technology has the ability to process and interpret a large amount of complicated data automatically. Besides, it is cheap as well as fast technology. Machine Learning has found its applications in areas like self-driving cars, facial recognition, recommendation engines, and facial recognition.

    According to payscale.com, a Machine Learning Engineer earns an average salary of $134,293 (USD). As a rising demand has been observed for the skilled Machine Learning professionals, candidates can take up this Machine Learning course in Mumbai to get the job competency and hands-on experience with Machine Learning.

  • What skills will you learn with this Machine Learning Training?

    The candidates taking the Machine Learning course in Mumbai will be able to do the following:

    • Be prepared to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
    • 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.
    • Enhance your knowledge of principles, algorithms, and applications of machine learning through a hands-on approach, which includes working on 28 projects and one Capstone project.
    • Learn the concepts of supervised, unsupervised, and reinforcement learning and modeling.
    • Explain the machine learning theoretical concepts with its practical aspects.
    • Explore the mathematical and heuristic aspects of machine learning.

  • Who should take this online Machine Learning Course?

    In all of the major industries, there is a considerable requirement for professionals who are proficient in Machine Learning. This Machine Learning Course is, therefore, ideal for professionals who have an intermediate level of working experience. The following professionals will specifically benefit from the Machine course:

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

     

  • What projects are included in this course?

     

    We make sure that our Machine Learning course provides coding experience along with hands-on projects. While beginning with the concepts, we also provide theoretical motivation and mathematical problem formulation.

    This course includes one primary capstone project and more than 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 the houses will be purchased or not by the consumers, from the given dataset, provided with their salary and age

    Project 1: In reference to the above problem statement, what issues can be observed in the plot generated by the code?

    Project  2: What is the estimated cost of the houses with areas 1700 and 1900?

    Concept covered: Data Preprocessing

    Case Study 2: Using the information provided in the dataset, demonstrate the methods to handle missing data, categorical data, and data standardization

    Project 3: Review the training dataset (Excel file). Observe that weight is missing for the fifth and eighth rows. For the mentioned rows, what are the values computed by the imputer?

    Project 4: In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and rerun it. 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 people over 55 years of age 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 the 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 result do you observe? Explain the reason.

    Case Study 7: Use Random Forests to predict insurance per year based on the age of a person.

    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 shows a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Interpret these charts.

    Project 19: The program shows the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try modifying the values to 0.001, 0.25, and 0.9 and observe the output. Give your interpretation.

    Concept Covered: Classification

    Case Study 9: Predict if the houses will be purchased by the consumers, given their salary and age. Use the information provided in the dataset

    Project 20: Typically, the nearest_neighbors for testing class in KNN has the value 5. Modify the code with the value of nearest_neighbours to 2 and 20, and note down your 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 to linear from RBF to check the type of classifier that is produced for the XOR data in this program. Interpret the data.

    Project 22:  For the Iris dataset, add new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Discuss the result.

    Case Study 11: Use Decision Trees to classify IRIS flower dataset. Use the information provided.

    Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and display 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:  Change the number of clusters k to 2, and record the observations.

    Project 27:  Modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. Find the output, and record the observations.

    Project 28:  Change the code to set the n_samples from 150 to 15000 and the number of centers to 4, keeping n_clusters at 4. Find the output.

    Project 29: Modify the number of clusters k to 6, and record your observations.

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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      • 1.2 Overview of Final Project 00:58
    • 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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      • 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 01-Creating and Printing an ndarray 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 Assignment 01 Demo 03:55
      • 5.12 Assignment 02
      • 5.13 Assignment 02 Demo 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 Assignment 01 Demo 01:20
      • 6.10 Assignment 02
      • 6.11 Assignment 02 Demo 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 Assignment 01 Demo 04:09
      • 7.13 Assignment 02
      • 7.14 Assignment 02 Demo 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 Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.19 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 Assignment 01 Demo 02:23
      • 10.9 Assignment 02
      • 10.10 Assignment 02 Demo 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 Demo3 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 Assignment 01 Demo 01:55
      • 11.11 Assignment 02
      • 11.12 Assignment 02 demo 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 01 - 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 02 - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Assignment 01 Demo 02:47
      • 12.11 Assignment 02
      • 12.12 Assignment 02 Demo 03:30
      • 12.13 Quiz
      • 12.14 Key takeaways 01:20
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Exam & certification

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

    Depending on the learning mode, Simplilearn's Machine Learning Course Completion Certificate can be obtained if:

    • The candidate completes one batch of Online Classroom training and finishes one project
    • The candidate completes 85% of the Self-Paced Course and finishes one project

  • What are the prerequisites for learning Machine Learning?

     

    The prerequisites for this Machine Learning course are:

    • Knowledge of basic high school mathematics
    • Clarity on the concepts of Python programming
    • Fundamental understanding of statistics

    Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

  • Who provides the certification?

     

    The Machine Learning Certification will be provided by Simplilearn to the candidates who complete the course successfully.

  • Is this course accredited?

     

    No, the Machine Learning course offered by Simplilearn is not accredited officially.

  • How long does it to take to complete the Machine Learning certification course?

     

    Candidates need to spend 45 - 50 hours of learning for the successful completion of the Machine Learning certification course.

  • How long does Simplilearn's certificate for machine learning course valid for?

     

    Simplilearn’s Machine Learning Certification is effective for the lifetime.

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

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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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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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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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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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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FAQs

  • What is the average salary for a Machine Learning Engineer in Mumbai?

    As per the survey conducted by Payscale, it is found that the average salary of Machine Learning engineers in Mumbai is Rs 1,047,479 per annum. It can increase significantly for professionals taking a Machine Learning training program.

  • What are different Machine Learning jobs & roles available in Mumbai?

    There are many roles belonging to the Machine Learning and Artificial Intelligence domain in Mumbai such as:

    • Senior Software Engineer ML
    • Data Scientist
    • ML Engineer
    • ML Specialist
    • Automation and Tool Development

  • Who are the top Employers Hiring Machine Learning Engineers in Mumbai?

     

    Machine Learning professionals are in high demand in Mumbai nowadays with companies like Morgan Stanley, Accenture, Ubisoft, Sutherland, JP Morgan Chase among the many creating opportunities for them.  

  • What is Machine Learning?

     

    Machine Learning is the next step of Artificial Intelligence in which systems are designed to learn from past experiences and provide improved results without programming it specifically. Machine Learning has the ability to analyze huge amounts of data and generate results with high speed and better accuracy giving profitable advantages. Basically, machine learning involves observation of data patterns, gathering crucial information, and making decisions that are efficient for the growth of any company.

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

     

    After a deduction of an amount equivalent to the administration fees, the remaining amount is refunded to the candidate on canceling the enrollment. For further queries, read our Refund Policy.

  • Are there any group discounts for classroom training programs?

     

    Yes, Simplilearn provides group discounts for its training programs. To get more details, visit the Simplilearn website and contact our support team via the Contact Us form or Live Chat option.

  • What are different payment options available to enroll for this course now?

     

    Candidates need to pay online to get themselves enrolled in our Machine Learning Course in Mumbai. The payment options available are:

    • American Express
    • PayPal
    • Visa Credit or Debit Card
    • Diner’s Club
    • MasterCard

    An automatically generated receipt will be sent to the candidate via email once the payment is successful.

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

     

    We provide a Contact Us form on the right of every page of our website. There is also an option of Live Chat where candidates can contact our support team or use the Help & Support portal.

  • What is Global Teaching Assistance?

     

    The faculty at Simplilearn enrich the learning experience of the students through interactive sessions and making sure that students have a firm grasp of the subject being taught. Teaching assistance is provided during business hours by our faculty who support the candidates right from class onboarding to project completion and job assistance.

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

     

    Our support team is available for the candidates via calls, chat, or email throughout the day. There is a community forum too that can be accessed 24/7 by the candidates. Moreover, it comes with permanent access.

  • What if I miss a class?

     

    Simplilearn makes it easier for its candidates to complete the Machine Learning course by providing the Flexi-pass. The Flexi-pass gives candidates an option to access 15 training sessions for 90 days that are conducted by highly experienced faculty. The candidates will get the benefit from both online classroom training and self-paced learning.

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

     

    The Machine Learning Course offered by Simplilearn involves completion of industry-oriented projects. To execute the projects, Simplilearn provides a cloud-based Python environment called as CloudLab. Candidates do not require a virtual machine to install and maintain Python and its libraries. They can use the browser to access a pre-configured environment on CloudLab.

    The CloudLab platform is accessible throughout the course completion via the Simplilearn Learning Management System (LMS)

     

     

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

     

    Candidates can either fill the Contact Us form or select the Live Chat option to get in touch with the support team. Both of the options are available on Simplilearn website. You can also click on Help & Support option.

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

     

    Yes, Simplilearn has a money-back guarantee for most of its training programs. Read our Refund Policy to know more. You can submit refund requests through our Help & Support portal.

  • What is online classroom training?

     

    Simplilearn conducts the Machine Learning Certification classes via live online streaming. These are the interactive sessions led by trainers with over 15 years of relevant experience, and the candidates can communicate with them to get their queries resolved.

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

     

    Simplilearn provides two learning methodologies. For self-paced e-learning, pre-recorded videos are provided to the candidates. With Flexi Pass learning, the candidates get the advantage of instructor-led live online training sessions along with pre-recorded videos.

  • Are the training and course material effective in preparing me for the Machine Learning certification?

     

    Yes, for successful completion of the Machine Learning Certification, we provide state-of-the-art training and course material.

  • Who are the instructors and how are they selected?

     

    We select trainers only after a strict recruitment procedure along with a high alumni rating. The process involves technical evaluation, profile screening as well as training demo. We ensure that the domain experts having several years of relevant teaching experience are only allowed to become the mentors.

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    Contact Us

    1800-212-7688

    (Toll Free)

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    Simplilearn Solutions Pvt Ltd, 601, 6th Floor, Rupa Solitaire, Millennium Business Park, Plot No.A-1, Mahape, Navi Mumbai - 400710, Maharashtra, India, Call us at: 1800-102-9602

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