Machine Learning Course Overview

The machine learning course in Shimla offered by us provides an in-depth overview of all vital Machine Learning topics. Your skillsets will be enhanced by working with real-time data and classification so that you derive the greatest benefits from our machine learning training course in Shimla. The machine learning course in Shimla is developed by industry-expert data scientists

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 domain is growing at a rapid pace. The industry growth rate is going through the roof,and that has resulted in a greater demand for skill-enhancing opportunities like a professional machine learning course in Shimla. Hence, a machine learning course in Shimla 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 Shimla
    Oracle hiring for Data Scientist professionals in Shimla
    Microsoft hiring for Data Scientist professionals in Shimla
    Amazon hiring for Data Scientist professionals in Shimla
    Walmart hiring for Data Scientist professionals in Shimla
    Source: Indeed
  • Annual Salary
    ₹501KMin
    ₹1594KAverage
    ₹7000KMax
    Source: Glassdoor
    Hiring Companies
    Dell hiring for Machine Learning Engineer professionals in Shimla
    Morgan Stanley hiring for Machine Learning Engineer professionals in Shimla
    Apple hiring for Machine Learning Engineer professionals in Shimla
    Google hiring for Machine Learning Engineer professionals in Shimla
    Accenture hiring for Machine Learning Engineer professionals in Shimla
    Source: Indeed

Training Options

Self-Paced Learning

₹ 18,999

  • Lifetime access to high-quality self-paced eLearning content curated by industry ML experts
  • 4 hands-on ML 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 Shimla from:-
4th Dec: Weekend Class
13th Dec: Weekday 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 training in Shimla is suitable for all candidates who want to learn more about working with data. Professionals possessing intermediate work skill levels can pick this informative machine learning training in Shimla, and that encompasses positions such as business analyst, information architects, analytics experts, and other related professions.
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Pre-requisites

The machine learning course in Shimla 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 have to already have a solid grasp on the fundamental courses such as Python for Data Science, Math Refresher, and other related areas of knowledge before they get into Machine Learning.
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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

      13:24
      • Project Highlights
        02:40
      • Andrew McAfee | Building Mind-Machine Combinations: Welcome Technology as Your Colleague
        05:22
      • 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 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

      19:46Preview
      • 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 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 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 Shimla
  • Who will provide the certification, and how long will it be valid for?

    When you complete our machine learning course in Shimla, you will earn a certificate of accreditation from our institute. The machine learning training in Shimla has lifelong validity and is recognized industry-wide.

  • How can I become a Machine Learning engineer?

    We provide you with a Machine Learning course in Shimla that will give you a total overview of the very topical and relevan field of Machine Learning. The basics offered are enough for you to bag a rewarding career. The machine learning training in Shimla 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 Shimla. 

    • 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, we will provide you with a one-shot practice test held as part of the machine learning course in Shimla module to get you ready for the real exam we give you for certification. You can also refer to the practice tests to get an idea of the actual course curriculum.

Machine Learning Training Reviews

  • Arjun Nemical

    Arjun Nemical

    Machine Learning Engineer, Bangalore

    The 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 course content is well-planned, comprehensive, and elaborate. Thank you, Simplilearn!

  • Kalpesh Mahajan

    Kalpesh Mahajan

    Pune

    I like the Simplilearn courses for the following reasons: It provides a unique blend of theoretical and practical based approaches. 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

    The 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 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 course very easily with good project exercises.

  • Somil Gadhwal

    Somil Gadhwal

    Application Engineer, Hyderabad

    Simplilearn's course content is designed in a way that every session is closely connected to the next. There is no need to mug up the lessons. The instructors put thought into training and motivating students. I am really happy I joined the course.

  • Afrid Mondal

    Afrid Mondal

    Nagpur

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

  • Kirandeep Kaur

    Kirandeep Kaur

    Bangalore

    Simplilearn's service is great. The 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 this 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 course 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!

  • 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.

  • 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!

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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 the salary of a Machine Learning Engineer in Shimla?

    In Shimla, the average pay for a Machine Learning Engineer is approximately ?699867 annually. With the most in-demand and up-to-date knowledge of Python, ML, Deep Learning, and NLP, etc. the Machine Learning Certification Shimla course propels your career to a higher position and pay.

  • What are the major companies hiring Machine Learning Engineers in Shimla?

    The demand for Machine Learning Engineers is exponentially increasing in Shimla. Machine Learning Engineers are always welcome in the following firms: Adobe, Hidden Brain, Aspiring Minds, MobiDev, InData Labs, and Fayrix, etc. You'll have no trouble getting into these ventures with a Machine Learning Certification Shimla on your resume.

  • What are the major industries in Shimla?

    Shimla's apples are sold to neighbouring Indian states. In reality, Shimla makes more money from exporting apples to countries outside of India. Bakery Products, Fruit Processing, Essential Oil, and Jute Bags are dominant in Shimla. You can be a part of these industries with Machine Learning Certification Shimla.

  • How to become a Machine Learning Engineer in Shimla?

    Working as a Machine Learning Engineer in Shimla is easy. To break into the area, you'll need a thorough understanding of Data Modeling & Evaluation, Applied Mathematics, and Neural Networks, and Machine Learning Certification Shimla will provide you with the necessary credentials.

  • How to find Machine Learning Certification in Shimla?

    After exploring the internet for Machine Learning Certification Shimla, you'll come across a slew of genuine sites where you may get the best bargain. Make sure to do thorough research and then work on your goal of becoming a Machine Learning Engineer in Shimla.

  • 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.

  • 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.

  • Is Machine learning a good career?

    Machine learning is one of the most in-demand career fields today. Present-day applications like driverless cars, facial recognition, voice assistants, and ecommerce recommendation engines are powered by machine learning. This field will be relevant going forward and professionals entering it can fetch lucrative salaries. As a first step, you can take our machine learning online course and learn everything from scratch.

  • 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.

  • 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.

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

    Simplilearn’s Machine Learning 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.

  • 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.

Machine Learning Training Course in Shimla

Shimla’s northernmost and southernmost points are Jakhoo and Annandale, respectively; the city's easternmost and westernmost points are Sanjauli and Chotta Shimla. The British-built Kalka–Shimla Railway line, which is a UNESCO World Heritage Site, is also a popular tourist destination. The population of Shimla was 169,578 as last recorded in 2011.

Shimla’s Gross Domestic Product (GDP) is ₹1.82 lakh crore in FY 2020-21, with a GDP per capita of ₹208,513 in 2018-19. Shimla holds the MTB Himalaya mountain biking race, which began in 2005 and is recognised as the largest event of its sort in South Asia, due to its high terrain. Don’t delay more and get hold of a Machine Learning Certification Shimla course.

Shimla is well linked to numerous places and is only 4 hours from Chandigarh, a nearby city. Although the city has an airport, there aren't many daily flights from here. Enroll in Machine Learning Certification Shimla course and avoid the regret of missing out on the city. Some of these destinations are:

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