Supervised Machine Learning Skills you will learn

  • Regression
  • Regularization
  • Classification
  • Clustering

Who should learn this Supervised Machine Learning course?

  • Aspiring Data Scientists
  • Software Engineers
  • Web developers
  • AIML enthusiasts

What you will learn in this Supervised Machine Learning course?

  • Introduction to Supervised & Unsupervised Machine Learning

    • Introduction

      00:50
      • Introduction
        00:50

    • Lesson 01: Basics of Machine Learning

      07:44
      • Basics of Machine Learning
        07:44

    • Lesson 02: Supervised Vs Unsupervised Learning

      14:35
      • Supervised Vs Unsupervised Learning
        14:35

    • Lesson 03: Linear and Logistic Regression

      01:00:34
      • Linear and Logistic Regression
        01:00:34

    • Lesson 04: Decision Tree and Random Forest

      01:06:52
      • Decision Tree and Random Forest
        01:06:52

    • Lesson 05: Naive Bayes and SVM

      01:06:41
      • Naive Bayes and SVM
        01:06:41

    • Lesson 06: K Nearest Clustering

      26:32
      • K Nearest Clustering
        26:32

    • Lesson 07: K means Clustering

      48:50
      • K means Clustering
        48:50

    • Lesson 08: PCA and Regularization

      59:48
      • PCA and Regularization
        59:48

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Why you should learn Supervised Machine Learning?

$8.81 billion

Expected Machine Learning market growth by 2022

44.1% growth

In the adoption of Machine Learning in organizations
 

About the Course

In this supervised machine learning course, you'll start with the basics of Machine Learning (ML), ensuring you have a solid grasp of its fundamental concepts. You'll also learn about clustering algorithms such as K Nearest Neighbor for grouping similar data points and K-means Clustering for partitioning data into clusters. Additionally, you'll discover dimensionality reduction techniques like Principal Component Analysis (PCA) and the importance of Regularization in preventing overfitting. By the end of the course, you'll have a clear understanding of these ML concepts a

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FAQs

  • What is supervised machine learning?

    Supervised learning is a subcategory of machine learning where algorithms are trained on labeled datasets to classify data or predict outcomes accurately. It relies on input-output pairs to learn patterns and make predictions.

  • Who should take this supervised machine learning course?

    This supervised machine learning course suits aspiring data scientists, software engineers, web developers, and anyone enthusiastic about artificial intelligence and machine learning.

  • Do I need any prerequisites to enroll in this supervised machine learning course?

    No prerequisites are required to enroll in this free course on supervised learning. However, having a basic understanding of mathematics, statistics, and programming is recommended for better comprehension.

  • Can I access the course materials after completing the course?

    Yes, you will have access to the course materials for 90 days after completing the course. This allows you to review the content and reinforce your learning at your own pace.

  • How long will I have access to the course materials?

    You will have access to the course materials 90 days after enrollment, giving you ample time to revisit the content and practice the skills you have learned.

  • What is the duration of the course?

    The course duration is 6 hours. It covers various topics in supervised machine learning to provide a comprehensive understanding of the subject.

  • How can I apply the skills learned in this course?

    You can apply the skills learned in this course to various real-world problems, such as classification, regression, and prediction tasks, in domains such as finance, healthcare, e-commerce, and more.

  • Is certification provided after course completion?

    Upon completing the supervised machine learning course, you will receive a certification that validates your understanding and proficiency in supervised machine learning concepts.

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