Course description

  • Why is Machine Learning Certification Course?

    • The companies are in an increasing requirement for the professionals to be aware of the ins and outs of Machine Learning as it is taking over the world.

    • There is an increasing need for professionals to be aware of Machine Learning concepts as this field is now taking over the world.

    • There is scope for the potential growth in the market size for Machine Learning from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022 at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

    Why learn Machine Learning

  • What are the objectives of this course?


    Machine Learning is a form of artificial intelligence that is transforming the world of computing together with people’s digital interactions. Machine Learning has become critical to numerous new and future applications by making it possible to cheaply, quickly, and automatically 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.

    The Machine Learning course in Bangalore prepares data scientists, engineers, and other professionals with the practical skills and knowledge required for job competency and certification in Machine Learning. There is a growing demand for Machine Learning skills around the globe. The average salary of a Machine Learning Engineer according to is $134,293 (USD).

  • What skills you learn with this Machine Learning Course?

    On completion of this Machine Learning Course in Bangalore, you can:

    • Get a thorough knowledge of the heuristic and mathematical aspects of machine learning.
    • Understand the theoretical concepts and their relation with the practical aspects of machine learning.
    • Get an introduction to machine learning & real-time applications
    • Model a wide variety of robust machine learning algorithms including recommendation systems, deep learning, and clustering.
    • Gain practical mastery over the algorithms, principles, and applications of machine learning with the help of a practical learning approach which involves working on 28 projects and one capstone project.
    • Be skillful in the concepts of reinforcement, unsupervised, and supervised learning concepts and modeling.
    • Understand the concepts and operation of decision tree classifier, random forest classifier, naive Bayes, support vector machines, K-nearest neighbors, kernel SVM, logistic regression, K-means clustering and more.

  • How much does this course costs in bangalore

    The cost of Simplilearn’s Machine Learning Certification training in Bangalore will be:

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

  • What is the Duration of this course in Bangalore?

    There are two learning methodologies for Simplilearn’s Machine Learning Course in Bangalore:

    • The first is the Self-paced e-Learning methodology with 6 months (180 days) validity. This provides candidates with high-quality e-learning video modules that let them work at their own pace.
    • The second methodology is the Online Classroom Flexi-Pass. Here, learners will get access to high-quality e-learning videos for a duration of 6 months (180 days) along with an access to 8+ instructor-led online training classes for a duration of 90 days.

  • Who should take this online machine learning certification training course?

    The demand for skilled Machine Learning engineers is increasing across all industries. This makes the Machine Learning certification course in Bangalore appropriate for candidates at an intermediate level of experience. Simplilearn particularly recommends this Machine Learning training in Bangalore for the following set of professionals:

    • Graduates eager to establish their career in machine learning and data science
    • Information architects who want to get skilled in the Machine Learning algorithms
    • Analytics managers who are leading a team of analysts
    • Developers aspiring to be a data scientist or machine learning engineer
    • Business analysts who want to understand data science techniques
    • 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

  • What are different job roles available in Machine learning Industry?

    The different job roles offered for the Machine Learning trained professionals in Bangalore are:

    • Core AI Engineers
    • Machine Learning scientists
    • Machine Learning Engineer
    • Data analysts
    • Data scientists

  • What projects are included in this Course?

    Simplilearn's Machine Learning Training course is practical and code-driven. The mathematical problem formulation and the theoretical motivation must be provided only while introducing the concepts.

    This course has a primary capstone project and more than 25 ancillary exercises based on the 17 machine learning algorithms.

    Capstone Project Details:

    Project Name: Predicting house prices in California

    Description: The project involves building a model that predicts average house values in Californian districts. Metrics such as median housing price, population, median income, and so on for each block group in California will be given to you. 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 that you build must be able to learn and predict the median housing price in any district.

    Concept covered: Techniques of Machine Learning

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

    Project 1: What issues do you see in the plot produced by the code in reference to 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: By using the information given in the dataset, illustrate the methods to handle categorical data, missing data, and data standardization.

    Project 3: Review the training dataset (Excel file). Observe that the weight for the fifth and eighth rows is missing. 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 re-execute 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: Illustrate how you can reduce the data dimensions from 3D to 2D by making use of 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 an explanation.

    Concept Covered: Regression

    Case Study 4: Show how you can reduce the data dimensions from 3D to 2D by making use of the information given

    Project 8: Alter the degree of the polynomial from the 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 the yearly insurance premium based on a person’s age by using the Decision Trees and the information provided in the dataset

    Project 11: Modify the code to predict insurance claim values for anyone above the age of 55 in the dataset provided.

    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 result obtained.

    Project 13: Modify the max_depth to 20, and observe the result

    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 given

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

    Case Study 7: Predict insurance per year based on a person’s age using Random Forests.

    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 depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your interpretation of these charts?

    Project 19: The program depicts the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try changing the values to 0.001, 0.25, and 0.9 and check the results? Provide interpretation.

    Concept Covered: Classification

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

    Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Modify the code to change the value of nearest_neighbours to 2 and 20, and note the 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 from RBF to linear to see the type of classifier that is 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: Classify IRIS flower dataset using Decision Trees. Use the information provided

    Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and show 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 given information

    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 note 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 note your observations.

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

    Project 29: Modify the number of clusters k to 6, and note 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

Course preview

    • 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
    • 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
    • Module 01 - Course Introduction 05:08
      • 1.1 Course Introduction 04:10
      • 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
    • Python Game Development - Create a Flappy Bird Clone 2:57:17
      • 1.1 Introduction to the Course and the Game 03:08
      • 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
    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science 08:42
      • 1.2 Different Sectors Using Data Science 05:59
      • 1.3 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique 00:57
      • 2.6 Data Analytics Conclusion or Predictions 04:30
      • 2.7 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.12 Testing 08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python 13:28
      • 4.4 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo -Creating and Printing an nd array 04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations 07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy 05:01
      • 5.10 Assignment 01
      • 5.11 Demo - Assignment 01 03:55
      • 5.12 Assignment 02
      • 5.13 Demo - Assignment 02 03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy 06:57
      • 6.2 SciPy Sub Package - Integration and Optimization 05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector 01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO 05:46
      • 6.8 Assignment 01
      • 6.9 Demo - Assignment 01 01:20
      • 6.10 Assignment 02
      • 6.11 Demo - Assignment 02 00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas 12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame 05:31
      • 7.4 View and Select Data Demo 05:34
      • 7.5 Missing Values 03:16
      • 7.6 Data Operations 09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support 00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation 02:00
      • 7.11 Assignment 01
      • 7.12 Demo - Assignment 01 04:09
      • 7.13 Assignment 02
      • 7.14 Demo - Assignment 02 02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach 03:57
      • 8.2 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works 01:24
      • 8.5 Steps 5 and 6 01:54
      • 8.6 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression 11:19
      • 8.11 Supervised Learning Models - Logistic Regression 08:43
      • 8.12 Unsupervised Learning Models 10:40
      • 8.13 Pipeline 02:37
      • 8.14 Model Persistence and Evaluation 05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Demo - Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.19 Demo - Assignment 02 05:14
      • 8.20 Quiz
      • 8.21 Key Takeaways 01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview 10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit 12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search 10:17
      • 9.7 Assignment 01
      • 9.8 Demo - Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.10 Demo - Assignment 02 08:00
      • 9.11 Quiz
      • 9.12 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization 08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots 10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots 09:34
      • 10.7 Assignment 01
      • 10.8 Demo - Assignment 01 02:23
      • 10.9 Assignment 02
      • 10.10 Demo - Assignment 02 01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing 12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree 12:56
      • 11.4 Navigating options
      • 11.5 Demo - Navigating a Tree 04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree 05:38
      • 11.8 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.10 Demo - Assignment 01 01:55
      • 11.11 Assignment 02
      • 11.12 Demo - Assignment 02 04:57
      • 11.13 Quiz
      • 11.14 Key takeaways 00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python 04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming 07:20
      • 12.4 Demo - Using Hadoop Streaming for Calculating Word Count 08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark 07:43
      • 12.7 Demo - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Demo - Assignment 01 02:47
      • 12.11 Assignment 02
      • 12.12 Demo - Assignment 02 03:30
      • 12.13 Quiz
      • 12.14 Key takeaways 01:20
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Exam & certification

  • When can you get Simplilearn's Machine Learning Training Completion Certificate?

    Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate that is globally recognized and accepted are:

    Online Self-Learning:

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

    Online Classroom:

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

  • What are the pre-requisites for attending this Machine Learning Training in Bangalore?


    For candidates to attend this Machine Learning training in Bangalore, they must:

    • Possess knowledge of basic high school mathematics  
    • Have an understanding of the fundamentals of Python programming
    • Know the basics of statistics

    The concepts of statistics and mathematics that needed for Machine Learning is covered in this course. When you purchase Simplilearn’s Machine Learning course, a complimentary Python course is also provided.

  • Who provides the certification?


    The certification will be awarded to the candidates after successfully completing this Machine Learning course.

  • Is this course accredited?


    No. This Machine Learning Course is not accredited by any standard organization.

  • How can i become machine learning Engineer?


    From this Machine Learning course, you can learn to programme in R language or Python; learn about real-time machine learning applications, implement machine learning using Tensorflow framework etc. Once you possess the basic skill set, you can gain practical experience by taking part in Kaggle competitions, applying for a Machine Learning internship, and completing personal engineering projects etc.

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

    Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate are:

    Online Self-Learning:

    • Complete one project.
    • Complete 85% of the course.

    Online Classroom:

    • Complete one project.
    • Attend a complete batch.

  • If I fail the Simplilearn's Certification Exam after completing Machine learning Course, how soon can I retake it?


    If you fail Simplilearn’s certification exam, you can retake it immediately.

  • Do you offer a money back guarantee for the training course?

    Yes.  For many training programs, Simplilearn offers a cash-back guarantee. You can submit refund requests through our Help and Support portal and also refer to our Refund Policy.

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. 



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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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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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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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.

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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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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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.

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


    The median annual salary of the Machine Learning engineer in Bangalore is Rs 12,28,665, according to Individuals who have undergone the Machine Learning training have the potential to earn more.

  • What are top companies that offer Machine Learning Jobs in Bangalore?

    Companies that are looking for skilled AI & Machine Learning experts in Bangalore are:

    • Siemens
    • Microsoft
    • JP Morgan
    • Amazon
    • Samsung R&D
    • Oracle
    • Chase
    • Netapp

  • What is Machine Learning?


    Machine learning, an implementation of Artificial Intelligence, enables systems to learn and improve at the same time from past experiences without the requirement for programming explicitly. It is the process of collecting relevant information, observing data patterns, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of large amounts of data, normally by delivering accurate and faster results to extract profitable opportunities and benefits.

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


    Yes, you can revoke your enrolment if it is necessary. After deducting the course fee we will provide a refund of your course fee. Please read our Refund Policy to learn more.

  • Are there any group discounts for classroom training programs?

    Yes, Simplilearn provides group discount options for the training programs. For further details, you can contact Simplilearn by selecting the Live Chat link or by making use of the form on the right side of any page on our website or get in touch with our customer service representatives.

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


    The enrollment for this training program can be done on the Simplilearn website and an online payment can be made through any one of the following methods:

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

    Once the payment is received, you will automatically get an email of the payment receipt along with the access information.

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


    Please contact Simplilearn by selecting the Live Chat link or by making use of the form on the right side of any page on our website. You can get more details from our customer service representatives.

  • What is Global Teaching Assistance?

    We have a dedicated group of subject matter experts as our teaching staff who will help you get certified in your first attempt. They proactively engage students and make sure that the course path is being followed. Our teaching assistants can also help in enriching your learning experience from class onboarding and project mentoring to job assistance. Teaching assistance is available during business hours.

  • What is Simplilearn's 24/7 Support promise?


    Simplilearn provides 24/7 support through telephone, chat, and email. Our dedicated team can provide you with an on-demand assistance through Simplilearn’s community forum. You will get a lifetime access to our community forum even after you complete the course with us.

  • What if I miss a class?


    With Simplilearn’s Flexi-pass, you can attend the classes without hampering your busy schedule. With this course, you will get an advantage of being trained by renowned trainers with an industry experience of many years. These trainers combine the best of self-paced learning with online classroom training. This Flexi-pass also provides you with access to 15 sessions for a duration of 90 days.

  • How will I execute projects during the course?


    Simplilearn offers CloudLab, which is a cloud-based Python environment lab along with this Machine Learning course for smooth execution of the practical projects. With this platform, candidates don’t have to install or maintain Python and it’s libraries on a virtual machine. They can instead access the pre-configured environment on CloudLab on your browser.

    From the Simplilearn Learning Management System (LMS), you will be given access to our online CloudLab platform for the duration of the course.

    You can complete the projects by making use of Simplilearn’s CloudLab

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


    You can contact Simplilearn by selecting the Live Chat link or by making use of the form on the right of any page on our website. You can also contact our Help & Support.

  • What is online classroom training?

    The online classroom training conducted for the Machine Learning Certification Course is through live online streaming. A Machine Learning certified instructor with training and working experience of more than 15 years handles the classes.

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

    • If candidates register for the online classroom Flexi Pass, they will be given access to the pre-recorded videos along with the live training that is conducted online.
    • If candidates enroll for self-paced e-learning, they can only access the pre-recorded videos.

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


    Yes, Simplilearn’s course materials and training are effective as they guarantee success in the Machine Learning certification exam.

  • Who are the instructors and how are they selected?


    Simplilearn’s highly qualified instructors are all industry experts with decades of relevant teaching experience. All the instructors have undergone a meticulous selection process including a training demo, technical evaluation, and profile screening before getting certified to train for us. It is also assured that instructors with a high alumni rating continue as our faculty.

  • 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.

    Our Bangalore Correspondence / Mailing address

    # 53/1 C, Manoj Arcade, 24th Main, Harlkunte, 2nd Sector, HSR Layout, Bangalore - 560102, Karnataka, India.

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