Machine Learning Algorithms Skills you will learn

  • Linear regression
  • Kmeans clustering
  • Logistic Regression
  • Decision tree
  • Random forest
  • PCA
  • Reinforcement learning
  • Q learning

Who should learn this Machine Learning Algorithms course?

  • Machine learning enthusiasts
  • Software engineers
  • Data scientists
  • Data analysts
  • Statisticians

What you will learn in this Machine Learning Algorithms course?

  • Getting Started with Machine Learning Algorithms

    • Introduction

      01:22
      • Introduction ML Algorithm
        01:22

    • Lesson 01: Introduction to Machine Learning

      07:08
      • Introduction to Machine Learning
        07:08

    • Lesson 02: Supervised Learning Algorithms- Linear Regression

      34:22
      • Supervised Learning Algorithms- Linear Regression
        34:22

    • Lesson 03: Logistic Regression

      30:33
      • Logistic Regression
        30:33

    • Lesson 04:Decision Tree

      24:45
      • Decision Tree
        24:45

    • Lesson 05: Random Forest

      40:11
      • Random Forest
        40:11

    • Lesson 06: Support Vector Machine (SVM)

      23:44
      • Support Vector Machine(SVM)
        23:44

    • Lesson 07: K Nearest Neighbors (KNN)

      26:31
      • K Nearest Neighbors(KNN)
        26:31

    • Lesson 08: Unsupervised Learning Algorithms- K means Clustering

      48:49
      • Unsupervised Learning Algorithms- K means Clustering
        48:49

    • Lesson 09: Principal component analysis (PCA)

      30:34
      • Principal component analysis(PCA)
        30:34

    • Lesson 10: Reinforcement Learning

      36:57
      • Reinforcement Learning
        36:57

    • Lesson 11: Q Learning

      21:38
      • Q Learning
        21:38

    • Knowledge Check

      • Knowledge Check

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Free Course to Learn Machine Learning Algorithms

Why you should learn Machine Learning Algorithms?

$31 billion by 2024

Worth of Machine Learning jobs globally

$112K

Average salary of a Machine Learning engineer

About The Course:

In this machine learning algorithms course, you'll explore the basics of Machine Learning. You'll start by understanding what ML is about and why it's important. Then, you'll dive into supervised learning, logistic regression, decision trees, and more. After that, you'll cover unsupervised learning with K-means Clustering and Principal Component Analysis. Lastly, you'll touch on reinforcement learning with Q Learning. By the end, you'll have a good grasp of ML algorithms and how they work.

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FAQs

  • What is a machine learning algorithm?

    A machine learning algorithm is a set of rules and techniques that allows computers to learn from data and make predictions or decisions. It helps AI systems perform tasks like classifying data or predicting outcomes based on input data.

  • How do machine learning algorithms work?

    Machine learning algorithms work by using data to learn patterns and relationships. They analyze large datasets to identify trends and make predictions without following explicit instructions.

  • What are some examples of machine learning algorithms?

    Examples of machine learning algorithms include Linear Regression, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machine.

  • Who should take this machine learning algorithm course?

    This machine learning algorithms course is ideal for anyone interested in machine learning, including beginners, software engineers, data scientists, data analysts, and statisticians who want to expand their knowledge and skills in machine learning techniques.

  • How long is the course?

    The course lasts 6 hours and comprehensively introduces machine learning algorithms.

  • Is there a certification upon completion?

    After finishing the course, you will receive a certificate that can significantly enhance your professional credentials.

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

    You will have access to the course materials for 90 days, giving you plenty of time to review and study the content at your own pace.

  • What are the prerequisites for enrolling in this machine learning algorithm course?

    There are no specific prerequisites for this course. However, a basic understanding of statistics, mathematics, and general machine-learning concepts will help you grasp the material more easily.

Learner Review

  • Ashish Sawant

    Ashish Sawant

    Assistant Professor , Prof Ram Meghe College of Engineering & Management,

    I had a great time exploring Machine Learning Algorithms through this course. Thank you, Simplilearn!

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