Introduction To Machine Learning
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The document provides an introduction to machine learning, describing its definition, stages, applications across various industries such as transportation, finance, healthcare, government, and retail. It outlines different types of machine learning algorithms including supervised, unsupervised, semi-supervised, and reinforcement learning, along with their specific use cases. The content emphasizes the importance of data collection, analysis, and model training in building effective machine learning solutions.
Introduction To Machine Learning
- 1. Introduction to Machine Learning Presented By: Rahul Khanna Software Consultant Knoldus Inc.
- 2. 01 What is Machine Learning 02 Stages of Machine Learning 03 Who is using Machine Learning ? 04 Types of Machine Learning Algorithms 05 Project: Analyze FIFA 19 Dataset Our Agenda
- 3. c What is Machine Learning 1. Machine learning is a method of data analysis that automates analytical model building 2. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Machine Learning is training a model from a data that generalizes a decision against a performance measure - Jason Brownlee The key to artificial intelligence has always been the representation. - Jeff Hawkins - Tom Mitchell
- 4. Collect Data 1. Identify Data Sets 2. Retrieve Data 3. Query Data Analyze 1. Select Analytical Techniques 2. Build Models Act Report 1. Create Visualizations 2. Communicate results Prepare Data 1. Explore Data 2. Preprocess Data Stages of Machine Learning
- 5. 1 2 3 4 5Transportation Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. Financial services Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud Health care Machine learning is a fast-growing trend in the healthcare industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Government Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Retail Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience Who is using Machine Learning ?
- 6. Supervised learning 01 Semisupervised learning03 Unsupervised learning 02 Reinforcement learning04 Types of Machine Learning Algorithms
- 7. c Supervised Learning These algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
- 8. c Unsupervised Learning Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
- 9. c Semi Supervised Learning It is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a webcam.
- 10. c Reinforcement Learning It is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best
- 16. Naïve Bayes
- 17. Decision Trees
- 18. K - Means
- 19. Apriori