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, 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 you learn in Machine Learning Certification 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 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 Machine Learning Real-time Projects you will complete during the 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.

  • What are the prerequisites for attending Machine Learning Training?


    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.

Course preview

    • Lesson 1: Introduction to Artificial Intelligence and Machine Learning

      • 1.01 Introduction to AI and Machine Learning
    • Lesson 2: Techniques of Machine Learning

      • 2.01 Techniques of Machine Learning
    • Lesson 3: Data Preprocessing

      • 3.01 Data Preprocessing
    • Lesson 4: Math Refresher

      • 4.01 Math Refresher
    • Lesson 5: Regression

      • 5.01 Regression
    • Lesson 6: Classification

      • 6.01 Classification
    • Lesson 7: Unsupervised learning - Clustering

      • 7.01 Unsupervised Learning with Clustering
    • Lesson 8: Introduction to Deep Learning

      • 8.01 Introduction to Deep Learning
    • Practice Projects

      • Uber Fare Prediction
      • Employee Access
      • Phishing detector with KNN
      • MNIST Classifier
    • Lesson 00 - Course Overview

      • 0.001 Course Overview
    • Lesson 01 - Data Science Overview

      • 1.001 Introduction to Data Science
      • 1.002 Different Sectors Using Data Science
      • 1.003 Purpose and Components of Python
      • 1.4 Quiz
      • 1.005 Key Takeaways
    • Lesson 02 - Data Analytics Overview

      • 2.001 Data Analytics Process
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique
      • 2.006 Data Analytics Conclusion or Predictions
      • 2.007 Data Analytics Communication
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting
      • 2.11 Quiz
      • 2.012 Key Takeaways
      • 2.10 Knowledge Check
    • Lesson 03 - Statistical Analysis and Business Applications

      • 3.001 Introduction to Statistics
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram
      • 3.11 Knowledge Check
      • 3.012 Testing
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics
      • 3.15 Quiz
      • 3.016 Key Takeaways
    • Lesson 04 - Python Environment Setup and Essentials

      • 4.001 Anaconda
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python
      • 4.004 Basic Operators and Functions
      • 4.5 Quiz
      • 4.006 Key Takeaways
    • Lesson 05 - Mathematical Computing with Python (NumPy)

      • 5.001 Introduction to Numpy
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo
      • 5.14 Quiz
      • 5.015 Key Takeaways
    • Lesson 06 - Scientific computing with Python (Scipy)

      • 6.001 Introduction to SciPy
      • 6.002 SciPy Sub Package - Integration and Optimization
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo
      • 6.12 Quiz
      • 6.013 Key Takeaways
    • Lesson 07 - Data Manipulation with Pandas

      • 7.001 Introduction to Pandas
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame
      • 7.004 View and Select Data Demo
      • 7.005 Missing Values
      • 7.006 Data Operations
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo
      • 7.15 Quiz
      • 7.016 Key Takeaways
    • Lesson 08 - Machine Learning with Scikit–Learn

      • 8.001 Machine Learning Approach
      • 8.002 Steps 1 and 2
      • 8.3 Steps 3 and 4
      • 8.004 How it Works
      • 8.005 Steps 5 and 6
      • 8.006 Supervised Learning Model Considerations
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression
      • 8.011 Supervised Learning Models - Logistic Regression
      • 8.012 Unsupervised Learning Models
      • 8.013 Pipeline
      • 8.014 Model Persistence and Evaluation
      • 8.16 Assignment 01
      • 8.15 Knowledge Check
      • 8.017 Assignment 01
      • 8.18 Assignment 02
      • 8.019 Assignment 02
      • 8.20 Quiz
      • 8.021 Key Takeaways
    • Lesson 09 - Natural Language Processing with Scikit Learn

      • 9.001 NLP Overview
      • 9.2 NLP Applications
      • 9.3 Knowledge Check
      • 9.004 NLP Libraries-Scikit
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02
      • 9.11 Quiz
      • 9.012 Key Takeaway
    • Lesson 10 - Data Visualization in Python using matplotlib

      • 10.001 Introduction to Data Visualization
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots
      • 10.5 Knowledge Check
      • 10.006 Types of Plots
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo
      • 10.11 Quiz
      • 10.012 Key Takeaways
    • Lesson 11 - Web Scraping with BeautifulSoup

      • 11.001 Web Scraping and Parsing
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree
      • 11.008 Parsing and Printing the Document
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo
      • 11.13 Quiz
      • 11.014 Key takeaways
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark

      • 12.001 Why Big Data Solutions are Provided for Python
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark
      • 12.007 Demo 02 - Using PySpark to Determine Word Count
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo
      • 12.13 Quiz
      • 12.014 Key takeaways
    • Math Refresher

      • Math Refresher
    • Lesson 1 Introduction

      • 1.1 Introduction
    • Lesson 2 Sample or population data

      • 2.1 Sample or population data
    • Lesson 3 The fundamentals of descriptive statistics

      • 3.1 The fundamentals of descriptive statistics
      • 3.2 Levels of measurement
      • 3.3 Categorical variables. Visualization techniques for categorical variables
      • 3.4 Numerical variables. Using a frequency distribution table
      • 3.5 Histogram charts
      • 3.6 Cross tables and scatter plots
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      • 4.1 Measures of central tendency, asymmetry, and variability
      • 4.2 Measuring skewness
      • 4.3 Measuring how data is spread out calculating variance
      • 4.4 Standard deviation and coefficient of variation
      • 4.5 Calculating and understanding covariance
      • 4.6 The correlation coefficient
    • Lesson 5 Practical example descriptive statistics

      • 5.1 Practical example descriptive statistics
    • Lesson 6 Distributions

      • 6.1 Distributions
      • 6.2 What is a distribution
      • 6.3 The Normal distribution
      • 6.4 The standard normal distribution
      • 6.5 Understanding the central limit theorem
      • 6.6 Standard error
    • Lesson 7 Estimators and Estimates

      • 7.1 Estimators and Estimates
      • 7.2 Confidence intervals - an invaluable tool for decision making
      • 7.3 Calculating confidence intervals within a population with a known variance
      • 7.4 Student’s T distribution
      • 7.5 Calculating confidence intervals within a population with an unknown variance
      • 7.6 What is a margin of error and why is it important in Statistics
    • Lesson 8 Confidence intervals advanced topics

      • 8.1 Confidence intervals advanced topics
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
    • Lesson 9 Practical example inferential statistics

      • 9.1 Practical example inferential statistics
    • Lesson 10 Hypothesis testing Introduction

      • 10.1 Hypothesis testing Introduction
      • 10.2 Establishing a rejection region and a significance level
      • 10.3 Type I error vs Type II error
    • Lesson 11 Hypothesis testing Let's start testing!

      • 11.1 Hypothesis testing Let's start testing!
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
      • 11.3 Test for the mean. Population variance unknown
      • 11.4 Test for the mean. Dependent samples
      • 11.5 Test for the mean. Independent samples (Part One)
      • 11.6 Test for the mean. Independent samples (Part Two)
    • Lesson 12 Practical example hypothesis testing

      • 12.1 Practical example hypothesis testing
    • Lesson 13 The fundamentals of regression analysis

      • 13.1 The fundamentals of regression analysis
      • 13.2 Correlation and causation
      • 13.3 The linear regression model made easy
      • 13.4 What is the difference between correlation and regression
      • 13.5 A geometrical representation of the linear regression model
      • 13.6 A practical example - Reinforced learning
    • Lesson 14 Subtleties of regression analysis

      • 14.1 Subtleties of regression analysis
      • 14.2 What is Rsquared and how does it help us
      • 14.3 The ordinary least squares setting and its practical applications
      • 14.4 Studying regression tables
      • 14.5 The multiple linear regression model
      • 14.6 Adjusted R-squared
      • 14.7 What does the F-statistic show us and why we need to understand it
    • Lesson 15 Assumptions for linear regression analysis

      • 15.1 Assumptions for linear regression analysis
      • 15.2 Linearity
      • 15.3 No endogeneity
      • 15.4 Normality and homoscedasticity
      • 15.5 No autocorrelation
      • 15.6 No multicollinearity
    • Lesson 16 Dealing with categorical data

      • 16.1 Dealing with categorical data
    • Lesson 17 Practical example regression analysis

      • 17.1 Practical example regression analysis
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Exam & certification FREE PRACTICE TEST

  • 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.

  • 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.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Machine Learning Multiple Choice Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.

Course advisor

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

​Named by Onalytica as the No.1 influencer in AI and 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 and Chief Data Scientist, CellStrat

Vivek is an entrepreneur and a 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. 



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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Asmita Wankhade
Asmita Wankhade Warangal

Course content is excellent. You can learn and understand, even if you are only a beginner. I am delighted to have joined and successfully finished the 'Certified Machine Learning’course. All thanks to Simplilearn.

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Sarita Visavalia
Sarita Visavalia Gyanam, Anand

I have enrolled in Data Science using Python and Machine Learning courses on Simplilearn. Course content is very well structured and relates to the current trends in technology and markets, as well. Each session is designed with real-time examples. Instructor-led training has given us excellent exposure. The trainer provides an understanding of all topics through practical exercises. The support team is very responsive. Thanks to Simplilearn team for the wonderful platform provided.

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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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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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    • 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.

    • 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.

    Our Mumbai Correspondence / Mailing address

    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

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