Supervised Machine Learning With Types And Techniques
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The document provides an overview of supervised machine learning, detailing its types, how it works, advantages, and disadvantages. Supervised learning deals with labeled input and output data to create predictive models for classification and regression problems, leading to accurate results. However, it can be complex, requiring significant computation time and pre-defined labels, unlike unsupervised learning which analyzes data in real time.
Supervised Machine Learning With Types And Techniques
- 1. Supervised Machine Learning with Types and Techniques Yo u r C o m p a n y N a m e
- 2. 2 Supervised Machine Learning01 o Types of Machine Learning o What is Supervised Machine Learning? o How Supervised Learning Works o Types of Supervised Machine Learning Algorithms o Supervised vs. Unsupervised Machine learning techniques o Advantages of Supervised Learning: o Disadvantages of Supervised Learning
- 3. Types of Machine Learning 3This slide is 100% editable. Adapt it to your needs and capture your audience's attention. Supervised Learning Unsupervised Learning Reinforcement Learning Inputs Outputs Rewards Inputs OutputsInputs Outputs Training Makes Machine Learn Explicitly Data with Clearly defined Output is given Direct feedback is given Predicts outcome/future Resolves Classification and Regression Problems Machine Understands the data (Identifies Patterns/ Structures) Evaluation is Qualitative or Indirect Does not Predict/Find anything Specific An approach to AI Reward Based Learning Learning form +ve & +ve Reinforcement Machine Learns how to act in a Certain Environment To Maximize Rewards
- 4. What is Supervised Machine Learning? 4 Input Raw Data Processing Output Algorithm Training Data set Desired Output Supervised Learning Supervisor This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 5. How Supervised Machine Learning works 5 Step 1 Provide the Machine Learning Algorithm Categorized or “labeled” Input and Output Data from to Learn Step 2 Feed the Machine New, Unlabeled Information to See if it Tags New Data Appropriately. If not, Continue Refining the Algorithm Machine Machine Group 1 Group 2 Types of Problems to which it’s Suited Classification Sorting Items into Categories Regression Identifying Real Values (Dollars, Weight, etc.) Label “Group 1” This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 6. Types of Supervised Machine Learning Algorithms 6 Classification o Fraud Detection o Email Spam Detection o Diagnostics o Image Classification Regression o Risk Assessment o Score Prediction Supervised Learning This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 7. Supervised vs. Unsupervised Machine Learning Techniques 7This slide is 100% editable. Adapt it to your needs and capture your audience's attention. Supervised Learning o Classification o Regression Input & Output Data Unsupervised Learning o Clustering o Association Input Data Predictions & Predictive Models Patterns / Structure Discovery
- 8. Advantages of Supervised Learning 8 It allows you to be very specific about the definition of the labels. In other words, you'll train the algorithm to differentiate different classes where you'll set a perfect decision boundary. You are ready to determine the amount of classes you would like to possess. The input file is extremely documented and is labeled. The results produced by the supervised method are more accurate and reliable as compared to the results produced by the unsupervised techniques of machine learning. this is often mainly because the input file within the supervised algorithm is documented and labeled. this is often a key difference between supervised and unsupervised learning. The answers within the analysis and therefore the output of your algorithm are likely to be known thanks to that each one the classes used are known. Advantages This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
- 9. Disadvantages of Supervised Learning 9 o Supervised learning are often a posh method as compared with the unsupervised method. The key reason is that you simply need to understand alright and label the inputs in supervised learning. o It doesn’t happen in real time while the unsupervised learning is about the important time. this is often also a serious difference between supervised and unsupervised learning. Supervised machine learning uses of-line analysis. o It is needed tons of computation time for training. o If you've got a dynamic big and growing data, you're unsure of the labels to predefine the principles. this will be a true challenge. This slide is 100% editable. Adapt it to your needs and capture your audience's attention.