Machine Learning Course Overview

This Machine Learning course in Delhi offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning training course to draw predictions from data.

Machine Learning Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Machine Learning course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • Gain expertise with 25+ hands-on exercises
  • 4 real-life industry projects with integrated labs
  • Dedicated mentoring sessions from industry experts
  • 58 hours of Applied Learning

Skills Covered

  • Supervised and unsupervised learning
  • Time series modeling
  • Linear and logistic regression
  • Kernel SVM
  • KMeans clustering
  • Naive Bayes
  • Decision tree
  • Random forest classifiers
  • Boosting and Bagging techniques
  • Deep Learning fundamentals

Benefits

The Machine Learning market is expected to reach USD $30.64 Billion by 2024, at a Compound Annual growth rate(CAGR) of 42.8-percent, indicating the increased adoption of Machine Learning among companies. By 2024, the demand for Machine Learning engineers is expected to grow by 11-percent.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    ₹509KMin
    ₹1364KAverage
    ₹4200KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Delhi
    Oracle hiring for Data Scientist professionals in Delhi
    Microsoft hiring for Data Scientist professionals in Delhi
    Amazon hiring for Data Scientist professionals in Delhi
    Walmart hiring for Data Scientist professionals in Delhi
    Source: Indeed
  • Annual Salary
    ₹501KMin
    ₹1594KAverage
    ₹7000KMax
    Source: Glassdoor
    Hiring Companies
    Dell hiring for Machine Learning Engineer professionals in Delhi
    Morgan Stanley hiring for Machine Learning Engineer professionals in Delhi
    Apple hiring for Machine Learning Engineer professionals in Delhi
    Google hiring for Machine Learning Engineer professionals in Delhi
    Accenture hiring for Machine Learning Engineer professionals in Delhi
    Source: Indeed

Training Options

Self-Paced Learning

₹ 18,999

  • Lifetime access to high-quality self-paced eLearning content curated by industry AI experts
  • 4 hands-on AI projects to perfect the skills learnt
  • Simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

online Bootcamp

₹ 20,999

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online Machine Learning training by top instructors and practitioners
  • Classes starting in Delhi from:-
23rd Aug: Weekday Class
4th Sep: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Machine Learning Course Curriculum

Eligibility

The Machine Learning course in Delhi is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.
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Pre-requisites

This Machine Learning course in Delhi requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.
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Course Content

  • Machine Learning

    Preview
    • Lesson 01 Course Introduction

      06:41Preview
      • Course Introduction
        05:31
      • Accessing Practice Lab
        01:10
    • Lesson 02 Introduction to AI and Machine Learning

      19:36Preview
      • 2.1 Learning Objectives
        00:43
      • 2.2 Emergence of Artificial Intelligence
        01:56
      • 2.3 Artificial Intelligence in Practice
        01:48
      • 2.4 Sci-Fi Movies with the Concept of AI
        00:22
      • 2.5 Recommender Systems
        00:45
      • 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:47
      • 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.8 Definition and Features of Machine Learning
        01:30
      • 2.9 Machine Learning Approaches
        01:48
      • 2.10 Machine Learning Techniques
        02:21
      • 2.11 Applications of Machine Learning: Part A
        01:34
      • 2.12 Applications of Machine Learning: Part B
        02:11
      • 2.13 Key Takeaways
        00:28
      • Knowledge Check
    • Lesson 03 Data Preprocessing

      35:57Preview
      • 3.1 Learning Objectives
        00:38
      • 3.2 Data Exploration Loading Files: Part A
        02:52
      • 3.2 Data Exploration Loading Files: Part B
        01:34
      • 3.3 Demo: Importing and Storing Data
        01:27
      • Practice: Automobile Data Exploration - A
      • 3.4 Data Exploration Techniques: Part A
        02:56
      • 3.5 Data Exploration Techniques: Part B
        02:47
      • 3.6 Seaborn
        02:18
      • 3.7 Demo: Correlation Analysis
        02:38
      • Practice: Automobile Data Exploration - B
      • 3.8 Data Wrangling
        01:27
      • 3.9 Missing Values in a Dataset
        01:55
      • 3.10 Outlier Values in a Dataset
        01:49
      • 3.11 Demo: Outlier and Missing Value Treatment
        04:18
      • Practice: Data Exploration - C
      • 3.12 Data Manipulation
        00:47
      • 3.13 Functionalities of Data Object in Python: Part A
        01:49
      • 3.14 Functionalities of Data Object in Python: Part B
        01:33
      • 3.15 Different Types of Joins
        01:32
      • 3.16 Typecasting
        01:23
      • 3.17 Demo: Labor Hours Comparison
        01:54
      • Practice: Data Manipulation
      • 3.18 Key Takeaways
        00:20
      • Knowledge Check
      • Storing Test Results
    • Lesson 04 Supervised Learning

      01:21:04Preview
      • 4.1 Learning Objectives
        00:31
      • 4.2 Supervised Learning
        02:17
      • 4.3 Supervised Learning- Real-Life Scenario
        00:53
      • 4.4 Understanding the Algorithm
        00:52
      • 4.5 Supervised Learning Flow
        01:50
      • 4.6 Types of Supervised Learning: Part A
        01:54
      • 4.7 Types of Supervised Learning: Part B
        02:03
      • 4.8 Types of Classification Algorithms
        01:01
      • 4.9 Types of Regression Algorithms: Part A
        03:20
      • 4.10 Regression Use Case
        00:34
      • 4.11 Accuracy Metrics
        01:23
      • 4.12 Cost Function
        01:48
      • 4.13 Evaluating Coefficients
        00:53
      • 4.14 Demo: Linear Regression
        13:47
      • Practice: Boston Homes - A
      • 4.15 Challenges in Prediction
        01:45
      • 4.16 Types of Regression Algorithms: Part B
        02:40
      • 4.17 Demo: Bigmart
        21:55
      • Practice: Boston Homes - B
      • 4.18 Logistic Regression: Part A
        01:58
      • 4.19 Logistic Regression: Part B
        01:38
      • 4.20 Sigmoid Probability
        02:05
      • 4.21 Accuracy Matrix
        01:36
      • 4.22 Demo: Survival of Titanic Passengers
        14:07
      • Practice: Iris Species
      • 4.23 Key Takeaways
        00:14
      • Knowledge Check
      • Health Insurance Cost
    • Lesson 05 Feature Engineering

      27:52Preview
      • 5.1 Learning Objectives
        00:27
      • 5.2 Feature Selection
        01:28
      • 5.3 Regression
        00:53
      • 5.4 Factor Analysis
        01:57
      • 5.5 Factor Analysis Process
        01:05
      • 5.6 Principal Component Analysis (PCA)
        02:31
      • 5.7 First Principal Component
        02:43
      • 5.8 Eigenvalues and PCA
        02:32
      • 5.9 Demo: Feature Reduction
        05:47
      • Practice: PCA Transformation
      • 5.10 Linear Discriminant Analysis
        02:27
      • 5.11 Maximum Separable Line
        00:44
      • 5.12 Find Maximum Separable Line
        03:12
      • 5.13 Demo: Labeled Feature Reduction
        01:53
      • Practice: LDA Transformation
      • 5.14 Key Takeaways
        00:13
      • Knowledge Check
      • Simplifying Cancer Treatment
    • Lesson 06 Supervised Learning Classification

      55:43Preview
      • 6.1 Learning Objectives
        00:34
      • 6.2 Overview of Classification
        02:05
      • Classification: A Supervised Learning Algorithm
        00:52
      • 6.4 Use Cases of Classification
        02:37
      • 6.5 Classification Algorithms
        00:16
      • 6.6 Decision Tree Classifier
        02:17
      • 6.7 Decision Tree Examples
        01:45
      • 6.8 Decision Tree Formation
        00:47
      • 6.9 Choosing the Classifier
        02:55
      • 6.10 Overfitting of Decision Trees
        01:00
      • 6.11 Random Forest Classifier- Bagging and Bootstrapping
        02:22
      • 6.12 Decision Tree and Random Forest Classifier
        01:06
      • Performance Measures: Confusion Matrix
        02:21
      • Performance Measures: Cost Matrix
        02:06
      • 6.15 Demo: Horse Survival
        08:30
      • Practice: Loan Risk Analysis
      • 6.16 Naive Bayes Classifier
        01:28
      • 6.17 Steps to Calculate Posterior Probability: Part A
        01:44
      • 6.18 Steps to Calculate Posterior Probability: Part B
        02:21
      • 6.19 Support Vector Machines : Linear Separability
        01:05
      • 6.20 Support Vector Machines : Classification Margin
        02:05
      • 6.21 Linear SVM : Mathematical Representation
        02:04
      • 6.22 Non-linear SVMs
        01:06
      • 6.23 The Kernel Trick
        01:19
      • 6.24 Demo: Voice Classification
        10:42
      • Practice: College Classification
      • 6.25 Key Takeaways
        00:16
      • Knowledge Check
      • Classify Kinematic Data
    • Lesson 07 Unsupervised Learning

      28:26Preview
      • 7.1 Learning Objectives
        00:29
      • 7.2 Overview
        01:48
      • 7.3 Example and Applications of Unsupervised Learning
        02:17
      • 7.4 Clustering
        01:49
      • 7.5 Hierarchical Clustering
        02:28
      • 7.6 Hierarchical Clustering Example
        02:01
      • 7.7 Demo: Clustering Animals
        05:39
      • Practice: Customer Segmentation
      • 7.8 K-means Clustering
        01:46
      • 7.9 Optimal Number of Clusters
        01:24
      • 7.10 Demo: Cluster Based Incentivization
        08:32
      • Practice: Image Segmentation
      • 7.11 Key Takeaways
        00:13
      • Knowledge Check
      • Clustering Image Data
    • Lesson 08 Time Series Modeling

      37:44Preview
      • 8.1 Learning Objectives
        00:24
      • 8.2 Overview of Time Series Modeling
        02:16
      • 8.3 Time Series Pattern Types: Part A
        02:16
      • 8.4 Time Series Pattern Types: Part B
        01:19
      • 8.5 White Noise
        01:07
      • 8.6 Stationarity
        02:13
      • 8.7 Removal of Non-Stationarity
        02:13
      • 8.8 Demo: Air Passengers - A
        14:33
      • Practice: Beer Production - A
      • 8.9 Time Series Models: Part A
        02:14
      • 8.10 Time Series Models: Part B
        01:28
      • 8.11 Time Series Models: Part C
        01:51
      • 8.12 Steps in Time Series Forecasting
        00:37
      • 8.13 Demo: Air Passengers - B
        05:01
      • Practice: Beer Production - B
      • 8.14 Key Takeaways
        00:12
      • Knowledge Check
      • IMF Commodity Price Forecast
    • Lesson 09 Ensemble Learning

      35:41Preview
      • 9.01 Ensemble Learning
        00:24
      • 9.2 Overview
        02:41
      • 9.3 Ensemble Learning Methods: Part A
        02:28
      • 9.4 Ensemble Learning Methods: Part B
        02:37
      • 9.5 Working of AdaBoost
        01:43
      • 9.6 AdaBoost Algorithm and Flowchart
        02:28
      • 9.7 Gradient Boosting
        02:36
      • 9.8 XGBoost
        02:23
      • 9.9 XGBoost Parameters: Part A
        03:15
      • 9.10 XGBoost Parameters: Part B
        02:30
      • 9.11 Demo: Pima Indians Diabetes
        04:14
      • Practice: Linearly Separable Species
      • 9.12 Model Selection
        02:08
      • 9.13 Common Splitting Strategies
        01:45
      • 9.14 Demo: Cross Validation
        04:18
      • Practice: Model Selection
      • 9.15 Key Takeaways
        00:11
      • Knowledge Check
      • Tuning Classifier Model with XGBoost
    • Lesson 10 Recommender Systems

      25:45Preview
      • 10.1 Learning Objectives
        00:28
      • 10.2 Introduction
        02:17
      • 10.3 Purposes of Recommender Systems
        00:45
      • 10.4 Paradigms of Recommender Systems
        02:45
      • 10.5 Collaborative Filtering: Part A
        02:14
      • 10.6 Collaborative Filtering: Part B
        01:58
      • 10.7 Association Rule Mining
        01:47
      • Association Rule Mining: Market Basket Analysis
        01:43
      • 10.9 Association Rule Generation: Apriori Algorithm
        00:53
      • 10.10 Apriori Algorithm Example: Part A
        02:11
      • 10.11 Apriori Algorithm Example: Part B
        01:18
      • 10.12 Apriori Algorithm: Rule Selection
        02:52
      • 10.13 Demo: User-Movie Recommendation Model
        04:19
      • Practice: Movie-Movie recommendation
      • 10.14 Key Takeaways
        00:15
      • Knowledge Check
      • Book Rental Recommendation
    • Lesson 11 Text Mining

      43:58Preview
      • 11.1 Learning Objectives
        00:22
      • 11.2 Overview of Text Mining
        02:11
      • 11.3 Significance of Text Mining
        01:26
      • 11.4 Applications of Text Mining
        02:23
      • 11.5 Natural Language ToolKit Library
        02:35
      • 11.6 Text Extraction and Preprocessing: Tokenization
        00:33
      • 11.7 Text Extraction and Preprocessing: N-grams
        00:55
      • 11.8 Text Extraction and Preprocessing: Stop Word Removal
        01:24
      • 11.9 Text Extraction and Preprocessing: Stemming
        00:44
      • 11.10 Text Extraction and Preprocessing: Lemmatization
        00:35
      • 11.11 Text Extraction and Preprocessing: POS Tagging
        01:17
      • 11.12 Text Extraction and Preprocessing: Named Entity Recognition
        00:54
      • 11.13 NLP Process Workflow
        00:53
      • 11.14 Demo: Processing Brown Corpus
        10:05
      • Wiki Corpus
      • 11.15 Structuring Sentences: Syntax
        01:54
      • 11.16 Rendering Syntax Trees
        00:55
      • 11.17 Structuring Sentences: Chunking and Chunk Parsing
        01:38
      • 11.18 NP and VP Chunk and Parser
        01:39
      • 11.19 Structuring Sentences: Chinking
        01:44
      • 11.20 Context-Free Grammar (CFG)
        01:56
      • 11.21 Demo: Structuring Sentences
        07:46
      • Practice: Airline Sentiment
      • 11.22 Key Takeaways
        00:09
      • Knowledge Check
      • FIFA World Cup
    • Lesson 12 Project Highlights

      02:40
      • Project Highlights
        02:40
      • Uber Fare Prediction
      • Amazon - Employee Access
    • Practice Projects

      • California Housing Price Prediction
      • Phishing Detector with LR
  • Free Course
  • Math Refresher

    Preview
    • Math Refresher

      30:35Preview
      • Math Refresher
        30:35
  • Free Course
  • Statistics Essential for Data Science

    Preview
    • Lesson 01: Course Introduction

      07:05Preview
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46
    • Lesson 02: Introduction to Statistics

      18:41Preview
      • 2.01 Learning Objectives
        01:16
      • 2.02 What Is Statistics
        01:50
      • 2.03 Why Statistics
        02:06
      • 2.04 Difference between Population and Sample
        01:21
      • 2.05 Different Types of Statistics
        02:42
      • 2.06 Importance of Statistical Concepts in Data Science
        03:20
      • 2.07 Application of Statistical Concepts in Business
        02:11
      • 2.08 Case Studies of Statistics Usage in Business
        03:09
      • 2.09 Recap
        00:46
    • Lesson 03: Understanding the Data

      17:29Preview
      • 3.01 Learning Objectives
        01:12
      • 3.02 Types of Data in Business Contexts
        02:11
      • 3.03 Data Categorization and Types of Data
        03:13
      • 3.03 Types of Data Collection
        02:14
      • 3.04 Types of Data
        02:01
      • 3.05 Structured vs. Unstructured Data
        01:46
      • 3.06 Sources of Data
        02:17
      • 3.07 Data Quality Issues
        01:38
      • 3.08 Recap
        00:57
    • Lesson 04: Descriptive Statistics

      32:03Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Mathematical and Positional Averages
        03:15
      • 4.03 Measures of Central Tendancy: Part A
        02:17
      • 4.04 Measures of Central Tendancy: Part B
        02:41
      • 4.05 Measures of Dispersion
        01:15
      • 4.06 Range Outliers Quartiles Deviation
        02:30
      • 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance
        02:52
      • 4.08 Z Score and Empirical Rule
        02:14
      • 4.09 Coefficient of Variation and Its Application
        02:06
      • 4.10 Measures of Shapes
        02:39
      • 4.11 Summarizing Data
        02:03
      • 4.12 Recap
        00:54
      • 4.13 Case Study One: Descriptive Statistics
        05:51
    • Lesson 05: Data Visualization

      20:55Preview
      • 5.01 Learning Objectives
        00:57
      • 5.02 Data Visualization
        02:15
      • 5.03 Basic Charts
        01:52
      • 5.04 Advanced Charts
        02:19
      • 5.05 Interpretation of the Charts
        02:57
      • 5.06 Selecting the Appropriate Chart
        02:25
      • 5.07 Charts Do's and Dont's
        02:47
      • 5.08 Story Telling With Charts
        01:29
      • 5.09 Recap
        00:50
      • 5.10 Case Study Two: Data Visualization
        03:04
    • Lesson 06: Probability

      19:46Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Key Terms in Probability
        02:25
      • 6.04 Conditional Probability
        02:11
      • 6.05 Types of Events Independent and Dependent
        02:56
      • 6.06 Addition Theorem of Probability
        01:58
      • 6.07 Multiplication Theorem of Probability
        02:08
      • 6.08 Bayes Theorem
        03:10
      • 6.09 Recap
        00:53
    • Lesson 07: Probability Distributions

      22:29Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Random Variable
        02:21
      • 7.03 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.04 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.05 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.06 Discrete Probability Distributions Poisson
        03:16
      • 7.07 Binomial by Poisson Theorem
        01:37
      • 7.08 Commonly Used Continuous Probability Distribution
        03:22
      • 7.09 Applicaton of Normal Distribution
        02:49
      • 7.10 Recap
        01:25
    • Lesson 08: Sampling and Sampling Techniques

      30:53Preview
      • 8.01 Learnning Objectives
        00:51
      • 8.02 Introduction to Sampling and Sampling Errors
        03:05
      • 8.03 Advantages and Disadvantages of Sampling
        01:31
      • 8.04 Probability Sampling Methods: Part A
        02:32
      • 8.05 Probability Sampling Methods: Part B
        02:27
      • 8.06 Non-Probability Sampling Methods: Part A
        01:42
      • 8.07 Non-Probability Sampling Methods: Part B
        01:25
      • 8.08 Uses of Probability Sampling and Non Probability Sampling
        02:08
      • 8.09 Sampling
        01:08
      • 8.10 Probability Distribution
        02:53
      • 8.11 Theorem Five Point One
        00:52
      • 8.12 Center Limit Theorem
        02:14
      • 8.13 Recap
        01:07
      • 8.14 Case Study Three: Sample and Sampling Technique
        05:16
      • 8.15 Spotlight
        01:42
    • Lesson 09: Inferential Statistics

      33:59Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.03 Null and Alternate Hypothesis
        01:44
      • 9.04 P Value
        03:22
      • 9.05 Levels of Significance
        01:16
      • 9.06 Type One and Two Errors
        01:37
      • 9.07 Z Test
        02:24
      • 9.08 Confidence Level Significance Level: Part A
        02:52
      • 9.09 Confidence Intervals: Part B
        01:20
      • 9.10 One Tail and Two Tail Tests
        04:43
      • 9.11 Notes to Remember for Null Hypothesis
        01:02
      • 9.12 Alternate Hypothesis
        01:51
      • 9.13 Recap
        00:56
      • 9.14 Case Study Four: Inferential Statistics
        06:24
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      27:07Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:14
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57
    • Lesson 11: Relation between Variables

      18:08Preview
      • 11.01 Learning Objectives
        01:06
      • 11.02 Correlation
        01:54
      • 11.03 Karl Pearson's Coefficient of Correlation
        02:36
      • 11.04 Karl Pearsons: Use Cases
        01:30
      • 11.05 Spearmans Rank Correlation Coefficient
        02:14
      • 11.06 Causation
        01:47
      • 11.07 Example of Regression
        02:28
      • 11.08 Coefficient of Determination
        01:12
      • 11.09 Quantifying Quality
        02:29
      • 11.10 Recap
        00:52
    • Lesson 12: Application of Statistics in Business

      17:25Preview
      • 12.01 Learning Objectives
        00:53
      • 12.02 How to Use Statistics In Day to Day Business
        03:29
      • 12.03 Example: How to Not Lie With Statistics
        02:34
      • 12.04 How to Not Lie With Statistics
        01:49
      • 12.05 Lying Through Visualizations
        02:15
      • 12.06 Lying About Relationships
        03:31
      • 12.07 Recap
        01:06
      • 12.08 Spotlight
        01:48
    • Lesson 13: Assisted Practice

      11:47
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

Industry Project

  • Project 1

    Fare Prediction for Uber

    Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies in building its next-generation model.

    Fare Prediction for Uber
  • Project 2

    Test bench time reduction for MercedesBenz

    Mercedes-Benz wants to shorten the time models spend on its test-bench, thus reducing a car’s time to market. Build and optimize a Machine Learning algorithm to solve this problem.

    Test bench time reduction for MercedesBenz
  • Project 3

    Income qualification prediction

    The Inter-American Development bank wants to qualify people for an aid program. Help the bank to build and improve the accuracy of the data set using a random forest classifier.

    Income qualification prediction
  • Project 4

    Access privileges prediction for Amazon employees

    Use the data of Amazon employees and their access permissions to build a model that automatically decides access privileges as employees enter and leave roles within Amazon.

    Access privileges prediction for Amazon employees
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Machine Learning Exam & Certification

Machine Learning Certificate in Delhi
  • Who provides the certificate and how long is it valid for?

    Upon successful completion of this Machine Learning training in Delhi, Simplilearn will provide you with an industry-recognized course completion certificate which has lifelong validity.

  • How do I become a Machine Learning Engineer?

    This Machine Learning course in Delhi will give you a complete overview of  ML methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. You will earn Simplilearn’s Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.

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

    Online Classroom:

    • Attend one complete batch of Machine Learning training in Delhi
    • 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 Machine Learning course?

    Yes, we provide 1 practice test as part of our Machine Learning 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.

Machine Learning Training Reviews

  • Arjun Nemical

    Arjun Nemical

    Machine Learning Engineer, Bangalore

    The Machine Learning training was awesome. The instructor has done a great job. He was very patient throughout the sessions and took additional time to explain the concepts further when we had queries.

  • Sharath Chenjeri

    Sharath Chenjeri

    Bangalore

    My trainer Sonal is amazing and very knowledgeable. The Machine Learning course content is well-planned, comprehensive, and elaborate. Thank you, Simplilearn!

  • Kalpesh Mahajan

    Kalpesh Mahajan

    Pune

    I like the Simplilearn Machine Learning course for the following reasons: It provides a unique blend of theoretical and practical based approach. 2. The learning pace is comfortable. 3. They have global industry experts as trainers.

  • Sharanya Nair

    Sharanya Nair

    Business Analyst, Bangalore

    I had completed Tableau, R, and Python training courses from Simplilearn. These courses helped a lot in moving ahead in my career path. Now, I am pursuing an MS in Data Science. Thank you, Simplilearn!

  • Ashok Kumar Kothandapani

    Ashok Kumar Kothandapani

    Chennai

    Simplilearn’s trainers are patient, clearing any confusion and answering all questions without impacting the course timeline. Simplilearn is the most convenient platform for those who want to grow in the fields of Machine Learning, Data Analytics and Data Science.

  • Jaya Raghavendra

    Jaya Raghavendra

    Mumbai

    I am a B.Sc Computers graduate. I had always attended physical classroom sessions, but this is the first time I experienced online classes. Simplilearn allowed learning from different mentors. Big thanks to the support team.

  • Asmita Wankhade

    Asmita Wankhade

    Warangal

    Machine Learning 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 Machine Learning certification. All thanks to Simplilearn.

  • Mahesh Gaonkar

    Mahesh Gaonkar

    Software Engineer, Bangalore

    Simplilearn is a great start for the beginner as well as 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 course very easily with good project exercises.

  • Afrid Mondal

    Afrid Mondal

    Nagpur

    The training was fantastic. Thank you for providing a great platform to learn.

  • Somil Gadhwal

    Somil Gadhwal

    Application Engineer, Hyderabad

    Simplilearn's Machine Learning 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.

  • Kirandeep Kaur

    Kirandeep Kaur

    Bangalore

    Simplilearn's service is great. The Machine Learning course instructor, Abhilash, was very cooperative. The sessions were interactive and exciting. Thank you, Simplilearn.

  • Tapas Bandyopadhyay

    Tapas Bandyopadhyay

    Senior Project Manager, Kolkata

    Simplilearn is the best platform to learn Machine Learning. I have enrolled in the Machine Learning course taught by Vaishali Balaji. Vaishali has excellent knowledge of the subject and covers all machine learning topics - from Linear Regression to XGBoost. The Online Labs are very useful too, for practice.

  • Akila Yukthi

    Akila Yukthi

    Chennai

    I had an incredible learning journey learning Simplilearn's Machine Learning certification under Vaishali Balaji. The course was successfully completed on time, and the trainer clarified all our doubts. Simplilearn is one of the best online platforms to learn Machine Learning! Thank you!

  • Ganesh N. Jorvekar

    Ganesh N. Jorvekar

    Mumbai

    I have enrolled in the PG program in Data Science with Simplilearn, and it has been a fantastic learning experience so far. Simplilearn has an excellent set of trainers who are competent enough to teach the new age technology. Thank you, Simplilearn, for such a great learning journey!

  • Parthiban Jayachandran

    Parthiban Jayachandran

    Bangalore

    I have enrolled in Simplilearn's Data Science and Advanced Machine Learning programs. The course content is comprehensive and live sessions enriching. Mentors are incredibly knowledgeable, and self-learning videos are helpful. The support team is accommodative and ready to help too.

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Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Machine Learning Training FAQs

  • 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.

  • Why learn Machine Learning?

    • 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

  • How do beginners learn Machine Learning?

    Machine learning is in high demand. But before you jump into certification training, it’s essential for beginners to get familiar with the basics of machine learning first. Simplilearn’s free resources articles, tutorials, and YouTube videos will help you get a handle on the concepts and techniques of machine learning. Start your learning with our free courses that serve as a foundation for this exciting and dynamic field: Statistics Essentials for Data Science, Math Refresher, and Data Science with Python.

  • Is this Machine Learning course training in Delhi suitable for freshers?

    Yes, the Machine Learning course in Delhi is suitable for freshers, and this course helps you learn Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling.

  • What is the course fees of the Machine Learning training in Delhi?

    The course fees of the Machine Learning training in Delhi start from Rs. 18,999/-.

  • In which areas of Delhi is the Machine Learning training conducted?

    No matter which area of Delhi you are in, be it Saket, Greater Kailash, Vasant Kunj, Lajpat Nagar, Gurgaon, Sunder Nagar, Dwarka, Shanti Niketan, Mayur Vihar, Golf Links, Vasant Vihar, Anand Lok, Udyog Vihar anywhere. You can access our Machine Learning course sitting at home or office.

  • Do you provide this Machine Learning course in Delhi with placement?

    No, currently, we do not provide any placement guarantee with the Machine Learning course.

  • Why do I need to choose Simplilearn to learn Machine Learning in Delhi?

    Simplilearn provides instructor-led training, lifetime access to self-paced learning, training from industry experts, and real-life industry projects with multiple video lessons.

  • What are the objectives of this Machine Learning course in Delhi?

    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 as innovative automated technologies as recommendation engines, facial recognition, fraud protection, and even self-driving cars.

    This Machine Learning course in Delhi prepares engineers, data scientists, and other professionals with the 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 INR 926K in Delhi, according to Glassdoor.com.
     

  • What skills will you learn with our Machine Learning course in Delhi?

    By the end of this Machine Learning course in Delhi, 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 course in Delhi?

    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 course in Delhi for the following professionals in particular:

    • Developers aspiring to be data scientists or machine learning engineers
    • 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 course in Delhi?

    Simplilearn's Machine Learning course in Delhi 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 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 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 17What 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 19The 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 10Classify the 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 new code at the end of this program to produce a classification for the RBF kernel trick with gamma = 1.0. Explain the output.
     
    Case Study 11: Classify the 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 13Demonstrate the 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 output.
    Project 29: Modify the number of clusters k to 6, and note the observations.

  • What are the prerequisites for this Machine Learning course in Delhi?

    Participants in this Machine Learning course in Delhi 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 course:

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

  • 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 700,000 a year. The earning potential can increase for individuals who have undergone a Machine Learning training.

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

    Other roles within the Machine Learning space available in Delhi are

    • Data Scientist
    • Research Scientist
    • Analyst Machine Learning

  • Which companies are hiring Machine Learning Engineers in Delhi?

    According to Naukri, companies like Expedia Group, American Express, Ernst & Young, Adove, etc are looking for skilled Machine Learning experts in Delhi.

  • How will the labs be conducted?

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

  • Is this will be a live training or pre-recorded videos?

    If you enroll in self-paced e-learning, you will have access to pre-recorded videos. If you enroll in the Online Bootcamp, you will have access to live Machine Learning training conducted online as well as the self-learning content.

  • What if I miss a class?

    Simplilearn provides recordings of each Machine Learning 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 Machine Learning trainers are industry AI 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 the Machine Learning 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 the 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 Machine Learning course with us.

  • How do I enroll in this Machine Learning course?

    You can enroll in this Machine Learning certification course 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.

  • * Disclaimer

    * The Machine Learning projects have been built leveraging real publicly available data sets of the mentioned organizations.

  • Why should I join an ML course by Simplilearn rather than other Machine Learning institutes in Delhi?

    Simplilearn combines a blended learning approach with hands-on exercises, industry projects, and integrated labs to give you a rich learning experience. Our Machine Learning course in Delhi includes a cutting-edge curriculum curated by industry experts to help you develop job-ready skills. Only a few other institutes offer such hands-on learning with capstone projects.

  • What are the localities in Delhi where Simplilearn provides the Machine Learning course?

    Simplilearn’s Machine Learning course in Delhi can be accessed online and taken anytime anywhere. We save your time and effort which you would otherwise spend in reaching a physical location.

  • Who should undergo this Machine Learning Course in Delhi?

    Any professional working at an intermediate level and willing to start a career in machine learning can take this course. It is also appropriate for data scientists, data analysts, business analysts, and information architects.

  • Why take Machine learning course in Delhi?

    The technology sector is growing rapidly in Delhi with many companies offering job opportunities in the field of machine learning. The job market has never been so competitive before and taking machine learning training is a wise decision for your career growth and to stay ahead of your peers.

  • What skillset must I possess to land a job in Machine Learning?

    If you are embarking on a career in machine learning, you should develop skills like statistics, basic programming, core mathematics, machine learning algorithms, libraries, and neural networks. Our Machine Learning course in Delhi will help you build all these skills.

  • What are the Machine Learning tools covered in this course?

    You get the benefit of learning hands-on tools like Python, XGBoost, Pandas, and TensorFlow when you enroll in Simplilearn’s Machine Learning training in Delhi.

  • Which mode of training is available for this Machine Learning course in Delhi?

    There are two training modes available for our Machine Learning course in Delhi. The first is self-paced learning in which the learner gets access to high-quality pre-recorded videos, projects, and simulation tests. The other is the online Bootcamp mode in which a learner can attend live online classroom training by top instructors apart from the materials offered in self-paced mode.

  • What algorithms will I learn in this Machine Learning course in Delhi?

    You will learn about some of the widely used machine learning algorithms like classification, decision trees, Naive Bayes, support vector machine (SVM), AdaBoost, Apriori, and clustering through our Machine Learning course in Delhi.

  • What makes a really good Machine Learning course?

    Machine learning is a complex field and learners need to be technically sound to start a career in it. So, a good Machine Learning course is something that involves not only theoretical concepts but one that focuses more on practical learning. It should include interactive quizzes, case studies, real-world industry projects, advanced machine learning tools, and virtual labs to enhance the learner’s knowledge and make him ready for a machine learning job.

  • What are the different types of Machine Learning?

    Machine learning is generally divided into three types - Supervised Learning, Unsupervised Learning, and Reinforcement Learning. This Machine Learning course gives you an in-depth understanding of all these three types of machine learning.

  • Does Machine Learning require coding?

    Yes, some coding knowledge is required to perform certain machine learning tasks like statistical analysis. Basic knowledge of either Python, R, or Java is recommended before taking this Machine Learning certification course.

  • What is the best language for Machine Learning?

    Machine Learning Engineers take into account various factors to decide which language would best suit their project. Their top choices include Python, C++, R, Java, and JavaScript.

  • What are the job roles available after getting a Machine Learning certification?

    Some of the top job roles in the field of Machine Learning are Data Scientist, Machine Learning Engineer, NLP Scientist, Computer Vision Engineer, and Data Architect. This Machine Learning course gives you all the necessary skills to become eligible for such roles.

  • Will this ML course help me to build a successful career in Machine Learning?

    Simplilearn’s Machine Learning certification course is designed by subject matter experts who know what skills are most valued by employers. Topics like types of machine learning, time series modeling, regression, classification, clustering, and deep learning basics are thoroughly covered, and allow you to start a career in this field.

  • Is there any university partnered program in Machine Learning?

    Professionals who take this Machine Learning course do not stop their learning and are inspired to learn more advanced machine learning concepts and seek to understand advanced AI and machine learning concepts as well. Simplilearn’s Post Graduate Program in AI and Machine Learning in partnership with the prestigious Purdue University is ideal for this purpose.

Our Delhi Correspondence / Mailing address

Simplilearn's Machine Learning Course in Delhi

District Centre 2nd Floor, KLJ Tower North, B-5 Netaji Subhash Place, Wazirpur New Delhi, Delhi 110034

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