Machine Learning Course Overview

When you take this machine learning course in Dallas, you get the chance to get some in-demand, timely topics under your belt, things like taking advantage of real-time data for the system, and time series modeling. This machine learning course in Dallas, along with machine learning training in Dallas, can provide you with the resources to achieve these skills.

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

This machine learning course in Dallas has tremendous future potential. Taking advantage of this machine learning course in Dallas and finishing the training in Dallas provides you with the opportunity to be a part of a huge wave of engineers who will be much in demand in the near future!

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

Machine Learning Course Curriculum

Eligibility

Anyone with previous intermediate-level knowledge in Machine Learning and AI, professionals like data analysts, data scientists, analytics managers, and developers, are already eligible for the machine learning training in Dallas. You can also do this course if you have a graduate degree in data science or computer science.
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Pre-requisites

The main requirement needed for this machine learning course in Dallas is possessing a well developed grasp and knowledge of fundamental statistics and mathematics at the university or college level. If you know programming languages like Python, it will benefit you a lot during the course. In order to get the most out the machine learning course in Dallas, it is important that you've already completed related fundamental courses like Math Refresher or Essential Statistics for Data Science.
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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

      10:31Preview
      • 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

      17:29
      • 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

      11:47
      • 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 Training Exam & Certification

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

    Upon completing the machine learning course in Dallas, you will be awarded accreditation and a well-recognized certificate for your accomplishments in machine learning training in Dallas. This certificate is valid for all institutions and companies around the world and stays valid for your entire lifetime. Rest assured, when you complete our machine learning course in Dallas, it will prove to be beneficial for the rest of your life.

  • How do I become an AI and Machine Learning Engineer?

    Becoming a proficient and capable AI and Machine Learning Engineer is extremely easy with our machine learning course in Dallas. The course gives you actual industry experience, which proves beneficial later on in your jobs. When you finish our machine learning training in Dallas, you show your future employers that you have the right skills in AI and Machine Learning, guaranteed! Our machine learning training in Dallas provides you with all the resources and skills like classification of data, its analysis, utilization of real-time data for your system, etc., that you need to be a successful AI and Machine Learning Engineer.

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

    If you are learning the machine learning course in Dallas through an Online Classroom:

    • Ensure that you sit and attend an entire batch of machine learning training in Dallas. 
    • Submit at least one completed project to our experts for skill verification. 

    If you are learning the machine learning course in Dallas through Online Self-Learning:

    • Finish at least 85% of the AI and Machine Learning course.
    • Deliver at least one finished project to our teaching experts.

  • Do you provide any practice tests as part of this AI and Machine Learning course?

    When you take the machine learning course in Dallas, you can be confident that you will be totally prepared for the real certification examination, thanks to the practice test we provide you with. If you want to get an idea of the type of tests you will be getting for your machine learning training in Dallas, you can check out our Machine Learning Free Practice Test. This practice test will give you an idea of the Multiple Choice Questions you can expect at the end of your machine learning training in Dallas.

    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 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.

    • 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.

    • Why learn Machine Learning?

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

    • Is this Machine Learning course in Dallas suitable for freshers?

      Yes, the Machine Learning course in Dallas 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 are the objectives of our Machine Learning course in Dallas?

      This Machine Learning course in Dallas will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

    • What skills will you learn in this Machine Learning training in Dallas?

      • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
      • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
      • Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
      • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
      • Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
      • Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

    • Who should take this Machine Learning course in Dallas?

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

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

    • What projects are included in this Machine Learning training in Dallas?

      Simplilearn’s Machine Learning course in Dallas is hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms. 

       

      Project 1: Fare Prediction for Uber

      Domain: Delivery (Commerce)
      Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.

       

      Project 2: Test bench time reduction for Mercedes-Benz

      Domain: Automobile

      Mercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimize the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective. 

       

      Project 3: Income qualification prediction for Inter-American Development bank

      Many social programs have a hard time making sure the right people are given enough aid.
      It’s tricky when a program focuses on the poorest segment of the population. This segment of
      population can’t provide the necessary income and expense records to prove that they qualify.
      Predicting the right set of people to be included for the aid remains a big challenge for  Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using a random forest classifier.

       

      Project 4: Access privileges prediction for Amazon.com employees

      There is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their
      provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.

    • What are the pre-requisites for attending this Machine Learning course in Dallas?

      Participants in this Machine Learning course in Dallas should have:

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

    • What is the price of the Machine Learning course in Dallas?

      The price of the Machine Learning course in Dallas starts from USD 699/-.

      Equated Monthly Installment (EMI): No cost EMI plans of 3, 6 & 12 months available. Contact us dialing the toll-free number: 844-532-7688 to avail of this payment option

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

      A Machine Learning Engineer can earn an average salary of $91,839 per year in Dallas, according to Glassdoor. Professionals who undertake Machine Learning training have even better salary prospects.

    • What are different job opportunities for Machine Learning professionals in Dallas?

      Professionals can apply for various job roles available in Dallas after they get certified in Machine Learning, such as:

      • AI Software Engineer

      • Data & Analytics Consultant

      • Analytics Production Engineer

      • Analytics Specialist

    • Which companies in Dallas are hiring certified Machine Learning professionals?

      Tech giants like Tiger Analytics, Amazon, Walmart, KPMG, Citibank, are in continuous demand for skilled Machine Learning Engineers in Dallas.

    • In which suburbs of Dallas is the Machine Learning training conducted?

      No matter which suburbs of Dallas you are in, be it Addison, Frisco, Farmers Branch, Plano, Carrollton, University Park, anywhere. You can access our Machine Learning course online sitting at home or office.

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

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

    • 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.

    • If I need to cancel my enrollment, can I get a refund?

      Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administrative fee. To learn more, please read our Refund Policy.

    • Who can I contact to learn more about this Machine Learning course?

      Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    • * Disclaimer

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

    • What is the salary of a Machine Learning Engineer in Dallas?

      A Machine Learning Engineer is responsible for creating programs and algorithms that enable machines to perform activities without human direction. The average salary offered to candidates with Machine Learning Certification in Dallas is $1,43,993/year.

    • What are the major companies hiring for Machine learning experts in Dallas?

      Machine Learning Certification in Dallas pays attention to developing algorithms that adapt to the data and take necessary actions. Several successful and recognized companies that recruit Machine Learning experts include Genentech, Tesla, Illumina, Apple, Facebook, Clair ways, etc.

    • What are the major industries in Dallas?

      AT&T, Carlson, Tenet Healthcare, Essilor of America, Southwest Airlines, Steward healthcare system, 7-Eleven, Texas Instruments, Blockbuster, Perot systems, etc., are the major companies in Dallas that employ candidates with Machine Learning Certification in Dallas. Dallas's top industries are technology, financial services, and defense.

    • How to become a Machine Learning Engineer in Dallas?

      Presently, the marked fields in technology are AI and ML. To become a Machine Learning Expert, an aspirant needs to get a Machine Learning Certification in Dallas to learn data concepts, statistical analysis and must have hypotheses for all queries and problems.

    • How do I find Machine Learning Certification Courses in Dallas?

      There are various courses that help aspirants in getting a Machine Learning Certification in Dallas. The concepts of machine learning seem familiar but need an in-depth understanding to design machine learning models. Choosing a course based on the curriculum, trainer, duration, and learning pace is wise.

    • 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.

    • 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

    • 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.

    Machine Learning Training in Dallas

    Dallas is a city situated in Texas, US. As per the estimation, in 2020, it is comprised of a population of roughly 1,304,379. It is the commercial and economic hub of the country, with a GDP amounting to $472.33 billion in the US. It has a prevailing GDP growth rate of 0.85℅ based on its first statistic recording in 2001. The GDP per capita is forecasted to be $66,757 in 2021. There are several small streams in the city. Dallas comprises two major lakes, namely White Rock Lake and Bachman Lake. The outskirts of the town have a larger body of water which is named Lake Ray Hubbard.

    Dallas attracts tourists with its landmarks that include Dallas Museum of Art, Dallas Zoo, The Dallas Arboretum, and Botanical Gardens, Perot Museum of Nature and Science, Six Flags over Texas, Nasher Sculpture Center, Klyde Warren Park, The Sixth Floor Museum at Dealey Plaza, the Dallas World Aquarium, the Reunion Tower, John F Kennedy Memorial, White Rock Lake, George W Bush Presidential Center, AT&T Stadium, Crow Museum of Asian Arts, Frontiers of Flight Museum, Meadows Museum, Cedar Hill State Park, Epic Waters Indoor Park, Dallas Heritage Village, African American museum, Trinity River Audubon Center, Dealey Plaza, Dallas Holocaust, and Human Rights Museum, Dallas Contemporary, Cedar River Preserve, etc.

    Our Dallas Correspondence / Mailing address

    Simplilearn's Machine Learning Training in Dallas

    14785 Preston Road, Suite 550 Dallas, TX 75254 United States

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