Machine Learning Course Overview

The machine learning course in Kolkata offered by us provides an in-depth overview of all vital Machine Learning topics. You will have the chance to get practical working experience with classification, real-time data, and other elements, empowering you to gain the most benefit from your machine learning training in Kolkata. The machine learning course in Kolkata is developed by industry-expert data scientists

Machine Learning Certification Key Features

  • 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 domain is growing at a rapid pace. The growth rate in this industry is sharply increasing,indicating a correspondingly higher increase in the demand for instruction, such as the professional machine learning course in Kolkata. Hence, a machine learning course in Kolkata will prove to be highly rewarding.

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

Machine Learning Course Curriculum

Eligibility

The machine learning training in Kolkata is suitable for all candidates who want to learn more about working with data. Intermediate-level professionals should consider this machine learning training in Kolkata,including information architects, analytics experts, business analysts, and many more.
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Pre-requisites

The machine learning course in Kolkata requires the candidate to know a few things before they get started. Aspirants should have basic knowledge of college-level mathematics and statistics. Familiarity with Python Programming will be a bonus. They need to have a working knowledge of every fundamental course like Python for Data Science, Math Refresher, and others, before they begin learning about Machine Learning.
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Course Content

  • Machine Learning

    Preview
    • Lesson 01: Course Introduction

      09:19Preview
      • 1.01 Course Introduction
        06:08
      • 1.02 Demo: Jupyter Lab Walk - Through
        03:11
    • Lesson 02: Introduction to Machine Learning

      08:40Preview
      • 2.01 Learning Objectives
        00:42
      • 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:46
      • 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.04 Definition and Features of Machine Learning
        01:30
      • 2.05 Machine Learning Approaches
        01:46
      • 2.06 Key Takeaways
        00:33
    • Lesson 03: Supervised Learning Regression and Classification

      02:10:59Preview
      • 3.01 Learning Objectives
        00:46
      • 3.02 Supervised Learning
        02:18
      • 3.03 Supervised Learning: Real Life Scenario
        00:55
      • 3.04 Understanding the Algorithm
        00:54
      • 3.05 Supervised Learning Flow
        01:51
      • 3.06 Types of Supervised Learning: Part A
        01:57
      • 3.07 Types of Supervised Learning: Part B
        02:05
      • 3.08 Types of Classification Algorithms
        01:03
      • 3.09 Types of Regression Algorithms: Part A
        03:23
      • 3.10 Regression Use Case
        00:36
      • 3.11 Accuracy Metrics
        01:24
      • 3.12 Cost Function
        01:49
      • 3.13 Evaluating Coefficients
        00:55
      • 3.14 Demo: Linear Regression
        13:48
      • 3.15 Challenges in Prediction
        01:47
      • 3.16 Types of Regression Algorithms: Part B
        02:40
      • 3.17 Demo: Bigmart
        37:29
      • 3.18 Logistic Regression: Part A
        02:01
      • 3.19 Logistic Regression: Part B
        01:41
      • 3.20 Sigmoid Probability
        02:07
      • 3.21 Accuracy Matrix
        01:28
      • 3.22 Demo: Survival of Titanic Passengers
        13:17
      • 3.23 Overview of Classification
        02:03
      • 3.24 Classification: A Supervised Learning Algorithm
        00:52
      • 3.25 Use Cases
        02:34
      • 3.26 Classification Algorithms
        00:17
      • 3.27 Performance Measures: Confusion Matrix
        02:21
      • 3.28 Performance Measures: Cost Matrix
        02:07
      • 3.29 Naive Bayes Classifier
        01:16
      • 3.30 Steps to Calculate Posterior Probability: Part A
        01:41
      • 3.31 Steps to Calculate Posterior Probability: Part B
        02:22
      • 3.32 Support Vector Machines: Linear Separability
        01:05
      • 3.33 Support Vector Machines: Classification Margin
        02:06
      • 3.34 Linear SVM: Mathematical Representation
        02:05
      • 3.35 Non linear SVMs
        01:07
      • 3.36 The Kernel Trick
        01:19
      • 3.37 Demo: Voice Classification
        10:42
      • 3.38 Key Takeaways
        00:48
    • Lesson 04: Decision Trees and Random Forest

      18:09Preview
      • 4.01 Learning Objectives
        00:37
      • 4.02 Decision Tree: Classifier
        02:17
      • 4.03 Decision Tree: Examples
        01:44
      • 4.04 Decision Tree: Formation
        00:46
      • 4.05 Choosing the Classifier
        02:56
      • 4.06 Overfitting of Decision Trees
        01:01
      • 4.07 Random Forest Classifier Bagging and Bootstrapping
        02:19
      • 4.08 Decision Tree and Random Forest Classifier
        01:07
      • 4.09 Demo: Horse Survival
        04:57
      • 4.10 Key Takeaways
        00:25
    • Lesson 05: Unsupervised Learning

      32:41Preview
      • 5.01 Learning Objectives
        00:36
      • 5.02 Overview
        01:47
      • 5.03 Example and Applications of Unsupervised Learning
        02:17
      • 5.04 Clustering
        01:46
      • 5.05 Hierarchical Clustering
        02:30
      • 5.06 Hierarchical Clustering: Example
        02:02
      • 5.07 Demo: Clustering Animals
        05:40
      • 5.08 K-means Clustering
        03:54
      • 5.09 Optimal Number of Clusters
        03:27
      • 5.10 Demo: Cluster Based Incentivization
        08:18
      • 5.11 Key Takeaways
        00:24
    • Lesson 06: Time Series Modelling

      38:57Preview
      • 6.01 Learning Objectives
        00:24
      • 6.02 Overview of Time Series Modeling
        02:16
      • 6.03 Time Series Pattern Types: Part A
        02:16
      • 6.04 Time Series Pattern Types: Part B
        01:19
      • 6.05 White Noise
        01:06
      • 6.06 Stationarity
        02:13
      • 6.07 Removal of Non Stationarity
        02:13
      • 6.08 Demo: Air Passengers I
        14:26
      • 6.09 Time Series Models: Part A
        02:14
      • 6.10 Time Series Models: Part B
        01:28
      • 6.11 Time Series Models: Part C
        01:51
      • 6.12 Steps in Time Series Forecasting
        00:37
      • 6.13 Demo: Air Passengers II
        06:14
      • 6.14 Key Takeaways
        00:20
    • Lesson 07: Ensemble Learning

      39:35Preview
      • 7.01 Learning Objectives
        00:24
      • 7.02 Overview
        02:41
      • 7.03 Ensemble Learning Methods: Part A
        02:49
      • 7.04 Ensemble Learning Methods: Part B
        04:09
      • 7.05 Working of AdaBoost
        01:43
      • 7.06 AdaBoost Algorithm and Flowchart
        02:28
      • 7.07 Gradient Boosting
        04:37
      • 7.08 XGBoost
        02:23
      • 7.09 XGBoost Parameters: Part A
        03:15
      • 7.10 XGBoost Parameters: Part B
        02:30
      • 7.11 Demo: Pima Indians Diabetes
        03:11
      • 7.12 Model Selection
        02:55
      • 7.13 Common Splitting Strategies
        01:45
      • 7.14 Demo: Cross Validation
        04:18
      • 7.15 Key Takeaways
        00:27
    • Lesson 08: Recommender Systems

      26:11Preview
      • 8.01 Learning Objectives
        00:27
      • 8.02 Introduction
        02:16
      • 8.03 Purposes of Recommender Systems
        00:45
      • 8.04 Paradigms of Recommender Systems
        02:45
      • 8.05 Collaborative Filtering: Part A
        02:14
      • 8.06 Collaborative Filtering: Part B
        01:58
      • 8.07 Association Rule: Mining
        01:47
      • 8.08 Association Rule: Mining Market Basket Analysis
        01:42
      • 8.09 Association Rule: Generation Apriori Algorithm
        00:53
      • 8.10 Apriori Algorithm Example: Part A
        02:13
      • 8.11 Apriori Algorithm Example: Part B
        01:17
      • 8.12 Apriori Algorithm: Rule Selection
        02:52
      • 8.13 Demo: User Movie Recommendation Model
        04:12
      • 8.14 Key Takeaways
        00:50
    • Lesson 09: Level Up Sessions

      10:31
      • Session 01
        05:22
      • Session 02
        05:09
    • Practice Project

      • 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

      32:48Preview
      • 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
        03:37
      • 4.08 Z Score and Empirical Rule
        02:14
      • 4.09 Coefficient of Variation and Its Application
        02:06
      • 4.10 Measures of Shape
        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

      19:49Preview
      • 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
        02:59
      • 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

      23:20Preview
      • 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
        02:28
      • 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 Techniques
        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 Intervals and Percentage 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 4: Inferential Statistics
        06:24
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      27:20Preview
      • 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
        04:27
      • 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

      11:47Preview
      • 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 Training Exam & Certification

  • Who will provide the certification, and how long will it be valid for?

    Upon completing the machine learning course in Kolkata, we will reward you with a certificate of accreditation. The machine learning training in Kolkata has lifelong validity and is recognized industry-wide.

  • How can I become a Machine Learning engineer?

    Our popular Machine Learning course in Kolkata provides you with a comprehensive overview of all the aspects of Machine Learning. The basics offered are enough for you to bag a rewarding career. The machine learning training in Kolkata will depict your skills and expertise in the domain. You will also get familiarized with classification, regression, clustering, and time series modeling.

  • How can I unlock my certificate?

    You will have to do the following to unlock your certificate for your machine learning course in Kolkata. 

    • Attend a complete class of machine learning training. 
    • Submit a project.

    If you are learning by yourself online, then you will have to.

    • Complete 85% of the course. 
    • Submit a completed project.

  • Do you offer practice tests as a part of the module of the course?

    Yes, as part of the machine learning course in Kolkata you get a single practice test to prepare our candidates to take the real certification exam. You can also refer to the practice tests to get an idea of the actual course curriculum.

    Machine Learning Course 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.

    • 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.

    • 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 ML 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 in Kolkata suitable for freshers?

      Yes, the Machine Learning course in Kolkata 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 fee of the Machine Learning training in Kolkata?

      The course fee of the Machine Learning training in Kolkata starts from Rs. 18,999/-.

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

      No matter which area of Kolkata you are in, be it Bidhannagar, Tollygunge, Ballygunge, Jadavpur, Dum Dum, Behala, Alipore anywhere. You can access our Machine Learning course sitting at home or office.

    • Do you provide this Machine Learning training in Kolkata 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 Kolkata?

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

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

      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 Kolkata 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 Kolkata where Simplilearn provides the Machine Learning course?

      Simplilearn’s Machine Learning course in Kolkata 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 training Kolkata?

      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 training in Kolkata?

      The technology sector is growing rapidly in Kolkata 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 Kolkata 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 course in Kolkata.

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

      There are two training modes available for our Machine Learning course in Kolkata. 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 training in Kolkata?

      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 Kolkata.

    • 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 is the average salary of a machine learning engineer in Kolkata?

      A machine learning engineer in Kolkata approximately earns ?800k in a year. The average salary of an employee may vary depending on his qualification, experience, and other certifications. Having a machine learning certification in Kolkata will help an individual to have more experience in the field.

    • What are the major companies hiring for machine learning engineers in Kolkata?

      The requirement for machine learning engineers is very high as machine learning is a key component of all automated technologies—some companies like IBM, Accenture in India, Codelogicx Technologies Private Limited, newAmps Consulting, 3rd. Life and ScaleneWorks People Solutions LLP are recruiting machine learning engineers in Kolkata. These firms are hiring individuals with machine learning certification in Kolkata.

    • What are the major industries in Kolkata?

      Numerous big government and private sector businesses have manufacturing sites in Kolkata. The major industries in Kolkata are jute processing, printing, publishing, engineering, information and technology, chemicals, and textiles. An individual with machine learning certification in Kolkata has career opportunities in the engineering and information, and technology industries.

    • How to become a machine learning engineer in Kolkata?

      Machine learning engineers combine two critical jobs: data scientist and software engineer. They are creating the way for the future of technology by allowing many sectors to harness new technologies. Getting a machine learning certification in Kolkata will help you grasp the complexity of the industry. Here’s how you can get started:

      • Earn a bachelor’s degree in computer science or any such related subject.
      • Learn programming languages, data structures, and algorithms, and relational databases.
      • Develop required technical skills, software skills, and data management skills.
      • Get different affirmations in the field to upgrade your insight and find a decent line of work.

    • How to find AI and machine learning courses in Kolkata?

      With the development in technology, AI and machine learning have become important fields in today’s world. To become a good machine learning engineer, it is necessary to have completed a primary course. Here are some ways for finding a decent course:

      • Find out on the web about good AI and machine learning courses and the colleges providing them.
      • Look for a college that is associated with well-known universities.
      • Take opinions of industry experts on other good courses.
      • Evaluate your options and choose a course and college that will be according to your requirements.

      After completing a course, you can also obtain machine learning certification in Kolkata to enhance your knowledge.

    • What is Machine Learning used for?

      Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

    • 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.

    • Are Machine Learning certifications worth it?

      Having a Machine Learning certification will help you gain the necessary knowledge and training to shape your career in an AI-led future and deal with machine learning problems.

    • What is the career exposure after completing this Machine Learning course?

      Machine learning has gained global traction and many are aspiring to start a career in this field. Jobs in AI and machine learning have grown around 75 percent over the past few years and Gartner predicts that there will be 2.3 million jobs in the field by 2022. Our ML course will give you all the necessary skills to work in this exciting field.

    • 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.

    • What does a Machine Learning Engineer do?

      The roles and responsibilities of Machine Learning Engineers include:

      • Designing and building machine learning systems and schemes
      • Analyzing and processing data science prototypes
      • Performing statistical analysis and modifying models using test results
      • Training ML systems whenever required and enhancing prevailing Machine Learning frameworks and libraries
      • Exploring new data to improve the machine’s performance

    • What skills should a Machine Learning Engineer know?

      A Machine Learning Engineer is expected to be skilled in areas like core math, statistics, basic programming, data modeling, neural networks, natural language processing, ML tools and libraries, and more. Our Machine Learning course will impart all of these skills and make you job-ready.

    • What is the difference between Machine Learning and Deep Learning?

      • Machine learning is a subtype of Artificial Intelligence, while deep learning is the evolved version of machine learning.
      • Deep learning is driven by neural networks that imitate neurons in the human brain, embedding a multi-layer architecture. In contrast, machine learning involves the usage of statistical methods to make a machine learn automatically through previously stored data patterns and without the requirement of programming or any human intervention.

    • What is the difference between Machine Learning and Artificial Intelligence?

      Artificial Intelligence is a broad field that encompasses everything that involves giving machines human-like intelligence. Machine learning is an important subset of AI where machines are given a lot of input data and algorithms are applied to train it and give them the ability to ‘learn’ and perform the desired actions. Our ML course deals with this topic in detail.

    • 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.

    • How is Simplilearn’s Machine Learning course syllabus better than other course providers?

      Simplilearn’s Machine Learning online course is based on a robust syllabus that equips you with extensive knowledge of machine learning concepts and trains you to:

      • Work on real-time data
      • Develop algorithms using both supervised and unsupervised learning methods
      • Create regression, classification, and time series modeling
      • Use Python to draw inferences from different data sets

      Upon completing a lesson, learners are taken through practice sessions to understand concepts better and gain practical knowledge. Additionally, the course offers fundamental courses like ‘Math Refresher’ and ‘Statistical Essential for Data Science’ for those who lack the basic knowledge required to take this course. Hence this is the best course for machine learning which you can opt.

    • 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 online 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.

    • What are the additional benefits I will get after enrolling in Simplilearn’s Machine Learning course?

      Simplilearn’s Machine Learning course offers additional benefits such as:

      • Access to in-depth knowledge of Machine Learning through 58 hours of applied learning, interactive labs, real-life, hands-on projects from Uber, Mercedes Benz, IDB, and 25+ hands-on exercises
      • Constant mentoring and assistance with the coursework from industry experts
      • Flexible training options in the form of self-paced learning, online bootcamp, or corporate training

    • 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.

    • 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.

    • * Disclaimer

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

    Machine Learning Course in Kolkata

    Kolkata (formerly Calcutta) represents West Bengal’s capital city. From 1773 until 1911, it functioned as India’s capital during the British occupation. The city originated as a trade center for the East India Company. As of 2021, Kolkata has a total population of 14,974,073 people. This city has the fourth highest population density in India. Kolkata city has an elevation of only 9.14 meters. Kolkata also has various nicknames like the city of joy, the city of palaces, and the cultural capital of India.

    Kolkata is India’s fifth-biggest economy, contributing considerably to the country’s GDP. As of 2020, India had a GDP of 2622.98 billion US dollars, with the per capita GDP being 1900.7 US dollars. The city’s climate can be classified as tropical, with summers being hot and humid and winters being cold and dry.

    Kolkata is a cultural leader in the fields of drama, arts, theatre, and literature, with numerous Nobel laureates adding to the city’s culture. Today this city is recognized for its magnificent colonial architecture, art galleries, and cultural events. Kolkata has many beautiful places and places to visit. There are also many beautiful bridges in the city. Some of the attractive places in Kolkata to visit are:

    Our Kolkata Correspondence / Mailing address

    Simplilearn's Machine Learning Course in Kolkata

    8th Floor, Plot K-1, & GP RDB Boulevard EP Block, Sector V, Bidhannagar Kolkata, West Bengal 700091

    View Location
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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.