Machine Learning Course Overview

Thanks to the skills you learn from this machine learning course in Johannesburg you gain the chance to develop a useful, working understanding of a bunch of valuable, career-enhancing topics like employing real-time data in the system and modeling time series. This machine learning course in Johannesburg, along with machine learning training in Johannesburg, 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 Johannesburg has tremendous future potential. Availing yourself of the machine learning course in Johannesburg and successfully finishing the machine learning training in Johannesburg will help you improve your career prospects by becoming one of the many engineers needed to fulfill the approaching high demands.

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

Machine Learning Course Curriculum

Eligibility

If you already have intermediate-level knowledge in Artificial Intelligence and Machine Learning, something characteristic of developers, data analysts, analytics managers, and data scientists, then you are eligible to take part in this machine learning training in Johannesburg. 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 chief necessary requirement you need to take advantage of this machine learning course in Johannesburg is having a fully developed understanding of mathematics and fundamental statistics at the college or university level. If you know programming languages like Python, it will benefit you a lot during the course. If you want to gain the full benefits from this AI and Machine Learning course in Johannesburg, it's mandatory that you already have taken and finished some prerequisite foundation courses like Essential Statistics or Math Refresher.
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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

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

      32:48Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Mathematical and Positional Averages
        03:15
      • 4.03 Measures of Central Tendancy: Part A
        02:17
      • 4.04 Measures of Central Tendancy: Part B
        02:41
      • 4.05 Measures of Dispersion
        01:15
      • 4.06 Range Outliers Quartiles Deviation
        02:30
      • 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance
        03:37
      • 4.08 Z Score and Empirical Rule
        02:14
      • 4.09 Coefficient of Variation and Its Application
        02:06
      • 4.10 Measures of Shape
        02:39
      • 4.11 Summarizing Data
        02:03
      • 4.12 Recap
        00:54
      • 4.13 Case Study One: Descriptive Statistics
        05:51
    • Lesson 05: Data Visualization

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

      19:49Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Key Terms in Probability
        02:25
      • 6.04 Conditional Probability
        02:11
      • 6.05 Types of Events: Independent and Dependent
        02:59
      • 6.06 Addition Theorem of Probability
        01:58
      • 6.07 Multiplication Theorem of Probability
        02:08
      • 6.08 Bayes Theorem
        03:10
      • 6.09 Recap
        00:53
    • Lesson 07: Probability Distributions

      23:20Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Random Variable
        02:21
      • 7.03 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.04 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.05 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.06 Discrete Probability Distributions: Poisson
        03:16
      • 7.07 Binomial by Poisson Theorem
        02:28
      • 7.08 Commonly Used Continuous Probability Distribution
        03:22
      • 7.09 Applicaton of Normal Distribution
        02:49
      • 7.10 Recap
        01:25
    • Lesson 08: Sampling and Sampling Techniques

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

      33:59Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.03 Null and Alternate Hypothesis
        01:44
      • 9.04 P Value
        03:22
      • 9.05 Levels of Significance
        01:16
      • 9.06 Type One and Two Errors
        01:37
      • 9.07 Z Test
        02:24
      • 9.08 Confidence Intervals and Percentage Significance Level: Part A
        02:52
      • 9.09 Confidence Intervals: Part B
        01:20
      • 9.10 One Tail and Two Tail Tests
        04:43
      • 9.11 Notes to Remember for Null Hypothesis
        01:02
      • 9.12 Alternate Hypothesis
        01:51
      • 9.13 Recap
        00:56
      • 9.14 Case Study 4: Inferential Statistics
        06:24
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      27:20Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-Parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:27
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57
    • Lesson 11: Relation between Variables

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

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

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

Industry Project

  • Project 1

    Fare Prediction for Uber

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

    Fare Prediction for Uber
  • Project 2

    Test bench time reduction for MercedesBenz

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

    Test bench time reduction for MercedesBenz
  • Project 3

    Income qualification prediction

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

    Income qualification prediction
  • Project 4

    Access privileges prediction for Amazon employees

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

    Access privileges prediction for Amazon employees
prevNext

Machine Learning Training Exam & Certification

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

    When you successfully complete our machine learning course in Johannesburg, you get accreditaton as well as a valuable, industry-recognized certificate that attests to your machine learning training in Johannesburg. 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 Johannesburg, 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 Johannesburg. The course gives you actual industry experience, which proves beneficial later on in your jobs. By successfully finishing the machine learning training in Johannesburg, you demonstrate and verify your acumen in AI and Machine Learning, valuable skills that many employers are looking for! Our machine learning training in Johannesburg 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 Johannesburg through an Online Classroom:

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

    If you are learning the machine learning course in Johannesburg 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?

    If you take our valuable machine learning course in Johannesburg, you will gain the benefit of the practice certification test offered as a perk in the course, getting you ready for the real certification examination, and giving you the confidence and peace of mind you need to pass! If you want to get an idea of the type of tests you will be getting for your machine learning training in Johannesburg, 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 Johannesburg.

    Machine Learning Course FAQs

    • What is Machine Learning?

      Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.

    • How will the labs be conducted?

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

    • How do beginners learn Machine Learning?

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

    • Is this Machine Learning course in Johannesburg, South Africa suitable for freshers?

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

    • What is the price of the Machine Learning course in Johannesburg, South Africa?

      The price of the Machine Learning course in Johannesburg, South Africa starts from $ 549.

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

      No matter which area of Johannesburg you are in, be it Sunninghill, Lonehill and Fourways, Sandton and Bryanston, Randburg, Northcliff and Melville, Linden, Parkhurst and Greenside, Rosebank and Parktown, Bedfordview, Maboneng, Newtown and Braamfontein anywhere. You can access our Machine Learning course online sitting at home or office.

    • Do you provide this Machine Learning course in South Africa 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 South Africa?

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

    • What is the salary of a machine learning engineer in Johannesburg?

      A machine learning engineer earns an annual remuneration package of around R434,450 on average in South Africa. Since Johannesburg is the top major city in South Africa, a machine learning engineer can earn equal to better salaries when compared to other cities in South Africa. But a good machine learning certification in Johannesburg and relevant fieldwork experience can help fetch a machine learning engineer an annual salary of up to R756,000 in Johannesburg.

    • What are the major companies hiring for a machine learning engineer in Johannesburg?

      Johannesburg's recognized machine learning certification can help you get good machine learning engineer jobs in companies like eSmart Group, Nedbank, C Ahead Info Technologies India, Founders Factory, and Reverside. Other than the above companies, many other companies are hiring smart and intellectual machine learning engineers in Johannesburg.

    • What are the major industries in Johannesburg?

      The stunning economy of Johannesburg is maintained by the top income generators of the city which include, heavy steel and cement industries, banking industry, IT, transport, private health care, broadcast, and print media.Machine learning engineering is a demanded skill in the industries mentioned above in Johannesburg, and they are actively recruiting diligent machine learning engineers with reputed machine learning certification in Johannesburg.

    • How to become a machine learning engineer in Johannesburg?

      Interested people can become machine learning engineers by completing a reputed machine learning certification in Johannesburg and then applying for job roles in top-tier companies that require the skill of a machine learning engineer in this city. A machine learning engineer should be able to deal with supervised and unsupervised learning and develop real-time algorithms related to machine learning.

    • How to find a machine learning certification course in Johannesburg?

      Interested candidates can easily find several online machine learning certification courses in Johannesburg. But you will need to make sure that the course satisfies the following criteria. A reputed course should have over 50 hours of applied learning. A good course should give you access to integrated labs, around four real-life industry projects, and over 25 hands-on exercises so you can gain expertise in skills like Kernel SVM, Decision tree, time series modeling, deep learning fundamentals, and more. Also, make sure that the course provides you with dedicated mentoring sessions with experts and 24/7 learner support.

    • What is Machine Learning used for?

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

    • What are the different types of Machine Learning?

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

    • Does Machine Learning require coding?

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

    • Are Machine Learning certifications worth it?

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

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

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

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

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

    • What does a Machine Learning Engineer do?

      The roles and responsibilities of Machine Learning Engineers include:

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

    • What skills should a Machine Learning Engineer know?

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

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

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

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

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

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

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

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

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

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

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

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

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

    • What if I miss a class?

      Simplilearn provides recordings of each Machine Learning class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.

    • Who are the instructors and how are they selected?

      All of our highly qualified Machine Learning trainers are industry AI experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

    • What is Global Teaching Assistance?

      Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in the Machine Learning in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

    • What is online classroom training?

      Online classroom training for the Machine Learning certification is conducted via online live streaming of each class. The classes are conducted by a Machine Learning certified trainer with more than 15 years of work and training experience.

    • What is covered under the 24/7 Support promise?

      We offer 24/7 support through email, chat, and telephone. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Machine Learning course online with us.

    • How do I enroll in this Machine Learning course?

      You can enroll in this Machine Learning certification course on our website and make an online payment using any of the following options:

      • Visa Credit or Debit Card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

      Once payment is received you will automatically receive a payment receipt and access information via email.

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

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

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

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

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

    • Is there any university partnered program in Machine Learning?

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

    • * Disclaimer

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

    Machine Learning Course in Johannesburg, South Africa

    Johannesburg is a capital megacity in South Africa spanning an area of 1,296 square miles with a population of 5.9 million. The area and population count make Johannesburg one of the largest and most populated urban cities. The stock exchange and the range of industries including gold and diamond mining make Johannesburg the commercial and industrial hub of South Africa. Johannesburg is located on the Highveld plateau at an elevation of 5,751 feet and has a subtropical highland climate. The GDP of Johannesburg has been projected to be $76 billion dollars and the per capita GDP is $16,370 thereby making the city best suited for a machine learning engineer.

    Johannesburg is a beautiful place to visit and is covered with a wide range of tourist attractions where people can have a good time with friends and family. The following are some of the most popular tourist attractions in Johannesburg:

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