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Course description

  • Why Machine Learning Certification Course?

    • Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
    • The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period
    Why learn Machine Learning

  • What are the objectives of this course?

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

    This Machine Learning online course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in machine learning. The demand for machine learning skills is growing quickly. The median salary of a Machine Learning Engineer is $134,293 (USD), according to payscale.com.
     

  • What skills will you learn with our Machine Learning Course?

    By the end of this Machine Learning course, you will be able to accomplish the following: 

    • Introduction to Machine Learning, Real-time Applications
    • Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
    • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
    • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
    • 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.
    • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
    • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems

     

  • Who should take this Machine Learning Training Course?

    There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

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

  • What projects are included in this Machine Learning Training Course?

    Simplilearn's Machine Learning Training course is very hands-on and code-driven. The theoretical motivation and Mathematical problem formulation must be provided only when introducing concepts.

    This course consists of one primary capstone project and 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 consumers will buy houses or not, from the given dataset,
    provided with 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 are the approximate prices of the houses with areas 1700 and 1900?
     
    Concept covered: Data Preprocessing
    Case Study 2: Demonstrate methods to handle missing data, categorical data, and data standardization using the information provided in the dataset
    Project 3: Review the training dataset (Excel file). Note that weight is missing 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 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 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 anyone above the age of 55 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 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 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 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:  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 centres 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 number of centres to 4, keeping n_clusters at 4. Check the output.
    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

  • Machine Learning
    • Lesson 1: Introduction to Artificial Intelligence and Machine Learning 32:24
      • 1.01- Introduction to AI and Machine Learning 32:24
    • Lesson 2: Techniques of Machine Learning 24:01
      • 2.01- Techniques of Machine Learning 24:01
    • Lesson 3: Data Preprocessing 1:15:56
      • 3.01- Data Preprocessing 1:15:56
    • Lesson 4: Math Refresher 30:40
      • 4.01- Math Refresher 30:40
    • Lesson 5: Regression 55:25
      • 5.01- Regression 55:25
    • Lesson 6: Classification 1:03:41
      • 6.01 Classification 1:03:41
    • Lesson 7: Unsupervised learning - Clustering 13:05
      • 7.01- Unsupervised Learning with Clustering 13:05
    • Lesson 8: Introduction to Deep Learning 10:03
      • 8.01- Introduction to Deep Learning 10:03
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    • Section 1 - Getting Started with Python 20:58
      • 1.1 Getting Started with Python 09:53
      • 1.2 Print and Strings 08:11
      • 1.3 Math 02:54
    • Section 2 - Variables, Loops and Statements 38:17
      • 2.1 Variables, Loops and Statements 04:58
      • 2.2 While Loops 06:13
      • 2.3 For Loops 05:13
      • 2.4 If Statments 06:59
      • 2.5 If Else Statements 04:12
      • 2.6 If Elif Else Statements 10:42
    • Section 3 - Functions and Variables 29:57
      • 3.1 Functions And Variables 05:21
      • 3.2 Function Parameters 15:00
      • 3.3 Global And Local Variables 09:36
    • Section 4 - Understanding Error Detection 12:29
      • 4.1 Understanding Error Detection 12:29
    • Section 5 - Working with Files and Classes 16:40
      • 5.1 Working With Files And Classes 04:45
      • 5.2 Appending To A File 03:29
      • 5.3 Reading From A File 03:47
      • 5.4 Classes 04:39
    • Section 6 - Intermediate Python 54:19
      • 6.1 Intermediate Python 07:55
      • 6.2 Import Syntax 06:53
      • 6.3 Making Modules 06:39
      • 6.4 Error Handling - Try And Accept 13:10
      • 6.5 Lists vs Tuples And List Manipulation 11:03
      • 6.6 Dictionaries 08:39
    • Section 7 - Conclusion 27:22
      • 7.1 Conclusion 27:22
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    • 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
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    • 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
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    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science 08:42
      • 1.2 Different Sectors Using Data Science 05:59
      • 1.3 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique 00:57
      • 2.6 Data Analytics Conclusion or Predictions 04:30
      • 2.7 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.12 Testing 08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python 13:28
      • 4.4 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo -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
  • Free Course
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Exam & certification

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

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

    Online Classroom:

    • Attend one complete batch
    • Submit at least one completed project.

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project.
       

Course advisor

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

​Named by Onalytica as the No.1 influencer in AI & Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

Vivek Singhal
Vivek Singhal Co-Founder & Chief Data Scientist, CellStrat

Vivek is an entrepreneur and thought Leader in Artificial Intelligence and deep-tech industries. He is a leading data scientist and researcher with expertise in AI, Machine Learning, and Deep Learning. 

 

Reviews

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.

Rajendra Kumar Rana
Rajendra Kumar Rana Senior Software Engineer RPA at Tech Mahindra, Pune

The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.

Anuvrat Kulkarni
Anuvrat Kulkarni Development Analyst in Social Media at Accenture, Bangalore

My experience with Simplilearn has been very enriching. The faculty was quite experienced and had a deep knowledge of the subject. I am happy with Simplilearn and would definitely recommend others.

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Deboleena Paul
Deboleena Paul Senior Technical Lead at HCL Technologies, Lucknow

My experience while doing machine learning certification from Simplilearnwas was beyond my expectation for an online classroom. The trainer was great. He was very patient and interactive.

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Parichay Bose
Parichay Bose Solutions Architect at Ericsson, Mississauga

I have been taking multiple courses from Simplilearn including Big Data Hadoop, Machine Learning, MEAN Stack. Apart from awesome content and trainer, they have amazing support executive that makes me feel cared. The customer support is helpful and is always there whenever you need help. That is where other online training programs are lagging behind. Well done Simplilearn!

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Vijay Marupadi
Vijay Marupadi Project Manager at Canadas Best Store Fixtures, Mississauga

The Simplilearn learning experience was beyond my expectation. The professionalism with which the training was carried out is worth commending. I would readily recommend Simplilearn to anyone who wants to pursue a career through online learning. It's worth the money. Happy learning with Simplilearn!

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FAQs

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

    According to PayScale, Machine Learning Engineers in Delhi can earn an average salary of Rs 959,706 a year. The earning potential can increase for individuals who have undergone a Machine Learning program.

  • What are other types of roles within the AI & Machine Learning space available in Delhi?

    Other roles within the AI & Machine Learning space available in Delhi are

    • Data Scientist
    • Research Scientist
    • Analyst Machine Learning

  • Which companies are hiring Machine Learning Engineers in Delhi?

    Companies like Untrodden Labs, Fareportal, AMEX, Infogain, Amazon, Gartner, Fidelity are looking for skilled AI & Machine Learning experts in Delhi.

  • 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.  

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

  • What is Machine Learning?

    Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.
     
     

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

    If you enrol for self-paced e-learning, you will have access to pre-recorded videos. If you enrol for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.

  • What 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.
     

  • 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.

     

  • How do I enrol for this online training?

    You can enrol for 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.

  • 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.

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    Contact Us

    1800-212-7688

    (Toll Free)

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    Our Delhi Correspondence / Mailing address

    Simplilearn Solutions Pvt Ltd, 2nd Floor, KLJ Tower North, B-5 District Centre, Netaji Subhash Place, Wazirpur, New Delhi – 110034, India, Call us at: 1800-102-9602

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