World’s #1 Online Bootcamp

Machine Learning Course Overview

This Machine Learning course in Chicago offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning training 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 a 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
  • 44 hours of instructor-led training with certification

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 $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Scientist
    Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture
    Oracle
    Microsoft
    Amazon
    Walmart
    Source: Indeed
  • Machine Learning Engineer
    Annual Salary
    $78KMin
    $114KAverage
    $150KMax
    Source: Glassdoor
    Hiring Companies
    Dell
    Morgan Stanley
    Apple
    Google
    Accenture
    Source: Indeed

Training Options

Self-Paced Learning

$ 699

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

online Bootcamp

$ 799

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Chicago from:-
7th Nov: Weekend Class
8th Nov: Weekday Class
Enroll now

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 Chicago 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 Chicago 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

      28:26
      • 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

      02:40Preview
      • 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

      30:36Preview
      • Math Refresher
        30:36
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 1 Introduction

      02:55Preview
      • 1.1 Introduction
        02:55

    • Lesson 2 Sample or population data

      03:56Preview
      • 2.1 Sample or population data
        03:56

    • Lesson 3 The fundamentals of descriptive statistics

      21:18Preview
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06

    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17Preview
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47

    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30

    • Lesson 6 Distributions

      16:17Preview
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19

    • Lesson 7 Estimators and Estimates

      23:36Preview
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38

    • Lesson 8 Confidence intervals advanced topics

      14:27
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24

    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37

    • Lesson 10 Hypothesis testing Introduction

      12:36Preview
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20

    • Lesson 11 Hypothesis testing Let's start testing!

      26:39Preview
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48

    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31

    • Lesson 13 The fundamentals of regression analysis

      18:32Preview
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36

    • Lesson 14 Subtleties of regression analysis

      23:25Preview
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00

    • Lesson 15 Assumptions for linear regression analysis

      19:16Preview
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22

    • Lesson 16 Dealing with categorical data

      05:20Preview
      • 16.1 Dealing with categorical data
        05:20

    • Lesson 17 Practical example regression analysis

      14:42Preview
      • 17.1 Practical example regression analysis
        14:42

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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 Training Course in Chicago

  • Who provides the certificate and how long is it valid for?

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

  • How do I become a Machine Learning Engineer?

    This course in Chicago will give you a complete overview of  Machine Learning 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 Chicago
    • 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 course?

    Yes, we provide 1 practice test as part of our 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 Course 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 Simplilearn courses 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 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

    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 'Certified Machine Learning’course. All thanks to Simplilearn.

  • Mahesh Gaonkar

    Mahesh Gaonkar

    Software Engineer, Bangalore

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

  • 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.

  • 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 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 Advanced Certification in Machine Learning 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 Data Science! 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.

  • Vijay Marupadi

    Vijay Marupadi

    Project Manager at Canadas Best Store Fixtures, Mississauga

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

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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 are the pre-requisites for attending this Machine Learning course?

    Participants in this Machine Learning course in Chicago should have:

    • Familiarity with the fundamentals of Python programming 
    • Fair understanding of the basics of statistics and mathematics

  • 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 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.

  • How will the labs be conducted?

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

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

    If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the Online Classroom Flexi-Pass, you will have access to live Machine Learning training conducted online as well as the pre-recorded videos.

  • 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 course with us.

  • How do I enroll in this Machine Learning online course?

    You can enroll in this Machine Learning online 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 projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Our Chicago Correspondence / Mailing address

Simplilearn's Machine Learning Training Course in Chicago | Address: 55 E. Monroe Street, Suite 3800, Chicago, IL - 60603, United States of America | Call us at +1-844-532-7688

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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.