Course description

  • Why Learn 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.
    • With Machine learning taking over the world, the companies greatly need professionals to be aware of the ins and outs of machine learning.
    Why learn Machine Learning

  • What are the objectives of this course?


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

    This Machine Learning course in Pune gives data scientists, engineers, and other professionals the hands-on skills and the knowledge required for job competency and a certification in machine learning. The demand for machine learning skills and knowledge is growing fast. According to, the average salary of a Machine Learning Engineer is $134,293 (USD).

  • What skills you learn in this Machine Learning Certification Course?

    On completing this Machine Learning course, you will:

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

  • Who should take this Machine Learning Training in Pune?

    Across industries, there is an increasing demand for skilled machine learning engineers. Skilled machine learning engineers are in demand throughout the globe and across all industries.

    This makes the Machine Learning certification course in Pune an ideal course for aspirants who have mid-level intermediate experience. We particularly recommend this Machine Learning training course for the following professionals:

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

  • How Much does Machine Learning Course Costs in pune?

    The price of Simplilearn’s Machine Learning Certification training is:

    • INR. RS.12999/- for the Self Paced Learning, and
    • INR Rs.20,999/- for Online Classroom Flexi-Pass.

  • What is the Duration of Machine Learning Certification Training in Pune?

    Simplilearn’s Machine Learning Certification Course in Pune has two learning methodologies:

    1. The first is the Self-paced e-Learning methodology with a validity of 180 days (6 months). This enables the students to work at their own pace through high-quality e-learning video modules.
    2. The second methodology is the Online Classroom Flexi-Pass with a validity of 180 days (6 months). This consists of high-quality e-learning videos along with access to 8+ instructor-led online training classes for 90 days.

  • What projects are included in this Course?


    Simplilearn's Machine Learning Training course is code-driven and provides hands-on experience. Mathematical problem formulation and theoretical motivation must be provided only while introducing the concepts.

    In this Machine Learning course, one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms have been included.

    Capstone Project Details:

    Project Name: Predicting house prices in California

    Description: The project involves building a model that predicts average house values in the Californian districts. The candidates will be given metrics such as average income, population, average housing price, and so on for each block group in California. Block groups are the smallest geographical units for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people). The model built by the candidates must learn from this data and be able to predict the average housing price in any district.

    Concept covered: Techniques of Machine Learning

    Case Study 1: From the given dataset, predict whether consumers will buy houses or not given their salary and age

    Project 1: What are the problems that you find in the plot produced by the code, on the basis of the above problem statement?

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

    Concept covered: Data Preprocessing

    Case Study 2: Illustrate methods to deal with categorical data, missing data, and data standardization using the information given in the dataset

    Project 3: Review the training dataset (Excel file). Note that the weights are not available for the fifth and eighth rows. What are the values computed by the imputer for these two missing rows?

    Project 4: In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and execute it again. What is the new value assigned to the blank fields Weight and Height for the two rows?

    Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as earlier?

    Case Study 3: Show how to reduce data dimensions from 3D to 2D from the given information

    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 explanation.

    Concept Covered: Regression

    Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D from the given information

    Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and depict the resulting regression plot. Mention if it is right-fitted, under fitted, or overfitted.

    Project 9: Determine the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.

    Project 10: From the code snippet given in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

    Case Study 5: Estimate the insurance premium per year based on a person’s age, by making use of the Decision Trees and the information provided in the dataset

    Project 11: Alter the code to determine insurance claim values for anyone above the age of 55 in the given dataset.

    Case Study 6: Demonstrate the Decision Tree regression and generate the random quadratic data

    Project 12: Observe the output on modifying the max_depth from 2 to 3 or 4

    Project 13: Discern the output after modifying the max_depth to 20.

    Project 14: What is the class prediction for petal_width = 1 cm and petal_length = 3 cm for max_depth = 2?

    Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes are present in the two cases? What does the average value represent in both the situations? Use the information given.

    Project 16: Adjust the regularization parameter min_sample_leaf from 10 to 6, and examine the output of Decision Tree regression. What is the result and why?

    Case Study 7: By making use of the Random Forests,  predict the insurance per year based on the person’s age.

    Project 17: What is the output insurance value for individuals aged 60 and with n_estimators = 10?

    Case Study 8:  Establish various regression techniques over a random dataset by making use of the information provided in the dataset

    Project 18: The program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your explanation for these charts.

    Project 19: The program represents the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try to alter the estimations to 0.001, 0.25, and 0.9 and examine the results. Provide an explanation.

    Concept Covered: Classification

    Case Study 9: Given the consumers' age and salary, find out if they will buy the houses. Utilize the data given in the dataset

    Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Alter the code to transform the value of nearest_neighbours to 2 and 20, and record the observations.

    Case Study 10: Classify IRIS dataset using SVM, and illustrate how Kernel SVMs can help classify non-linear data.

    Project 21: Alter the kernel trick from RBF to linear to observe the type of classifier produced for the XOR data in this program. Interpret the data.

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

    Case Study 11: Using Decision Trees classify IRIS flower dataset. Make use of the information given

    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 utilizing the various classification algorithms. Make use of the information given

    Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms.

    Concept Covered: Unsupervised Learning with Clustering

    Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.

    Project 26:  Modify the number of clusters k to 2, and 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. Check the output, and record your observations.

    Project 28:  Modify the code to change the number of centers to 4 and the n_samples from 150 to 15000, keeping n_clusters at 4. Examine the output.

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

  • 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

    And, here are some of the fundamental courses that participants need to know before they get into Machine Learning online course:

    • Python for Data Science
    • Math Refresher
    • Statistics Essential for Data Science

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

  • How will you get Simplilearn's Machine Learning Course Completion Certificate?

    Some of the qualifications you must satisfy to gain eligibility for the globally recognize Simplilearn’s course completion Certificate are:  

    Online Classroom:

    • Completion of one project.
    • Attending one complete batch.

    Online Self-Learning:

    • Completion of one project.
    • Completing 85% of the course.

  • What are the prerequisites for learning machine learning?


    Candidates in this Machine Learning online course should:

    • Have an understanding of the basics of statistics
    • Be aware of basic high school mathematics
    • Be familiar with the fundamentals of Python programming

    This course covers the concepts of statistics and mathematics that are necessary for machine learning and Simplilearn will provide you with a free Python course on purchasing our Machine Learning Certification course.

  • Who provides the certification?


    Simplilearn will award you the machine learning certification after successfully completing this course.

  • Is this course accredited?

    No. This Machine Learning Certification course is not certified officially by any standard organization.

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


    The successful completion time for this Machine Learning certification course is about 40-50 hours.

  • How can i become machine learning Engineer?


    You can become a machine learning engineer by learning to code in R language or Python, implementation of machine learning using the Tensorflow framework, real-time machine learning applications, etc. By making use of this online machine learning course you can consider getting a Programming language certification and the degree to become a more valuable candidate. Once you have a basic skill set, you can get experienced by completing personal engineering projects, taking up the machine learning internship and participating in Kaggle competitions etc.

  • How will you get Simplilearn's Machine Course Completion Certificate?

    Some of the qualifications you must satisfy to gain eligibility for the globally recognize Simplilearn’s course completion Certificate are:  

    Online Classroom:

    • Completion of one project and one simulation test with a minimum score of 80%.
    • Attending one complete batch.

    Online Self-Learning:

    • Completion of one project and one simulation test with a minimum score of 80%.
    • Completing 85% of the course.

  • Is this course accredited?

    No, this course is not officially accredited by any standard or organization.

  • How do I pass Simplilearn's Machine Learning Course Certification exam?

    • Online Self-learning: Complete one project and one simulation test with a minimum score of 80% and complete 85% of the course
    • Online Classroom: Complete one project and one simulation test with a minimum score of 80% and attend one complete batch

  • How long is the Machine Learning course certificate from Simplilearn valid for?


    Machine Learning Course Certificate by Simplilearn has a lifelong validity.

  • 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.


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.

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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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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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.

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 Average Salary companies pay for a Machine Learning Professionals in Pune?


    Machine Learning engineers have a median salary of 8 lakhs per year in Pune, according to the analysis by Payscale and other top research firms like Glassdoor. However, an experienced Machine Learning professional can earn up to 18 lakhs per year. It varies depending on the total experience, city, and more. Simplilearn’s Machine Learning Engineer Training enables ML professionals to request for salary hike beyond the average when they shift in the Market or can increase their earning potential if they are already employed.

  • What roles companies offer for machine learning professionals in Pune?

    Within the AI & Machine Learning space available in Pune, the other roles are:

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

  • Which companies are hiring Machine Learning Engineers in Pune?

    The companies that hire skilled AI & Machine Learning experts in Pune are:

    • Qualys,
    • NVIDIA,
    • Han Digital Solution,
    • TOMTOM, and
    • Barclays.

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

    Yes, cancellation of your enrolment is possible if necessary. Simplilearn will refund your course fee after deducting the administrative fee. Read our Refund Policy for more details.

  • Are there any group discounts for classroom training programs?


    Yes, group discount options are available for our training programs. You can contact Simplilearn by selecting the Live Chat link or using the form on the right of any page on our website. You can also get additional details from our customer service representatives.

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

    If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll 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.

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. 


    Our Pune Correspondence / Mailing address

    Simplilearn Solutions Pvt Ltd, 6th Floor, Pentagon P-2, Magarpatta City, Hadapsar, Pune - 411013, 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.