Machine Learning In Python | Python Machine Learning Tutorial | Deep Learning Python | Edureka
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The document provides an overview of machine learning, including its definition, process flow, types (supervised, unsupervised, reinforcement), and applications. It details the machine learning process, such as building predictive models and refining them through feedback, along with a specific example of linear regression to predict housing prices. Additionally, it outlines the necessary data components and methodologies used in training and evaluating machine learning models.
Machine Learning In Python | Python Machine Learning Tutorial | Deep Learning Python | Edureka
- 1. Machine Learning & Regression
- 2. Agenda ✓ What is Machine Learning and its process flow ✓ Machine Learning Applications ✓ Types of Machine Learning ✓ Demo – Linear Regression
- 3. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What is Machine Learning?
- 4. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What is Machine Learning? Machine learning is a subset of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning uses data to detect patterns and create a model and adjust program actions accordingly How to achieve?
- 5. Copyright © 2017, edureka and/or its affiliates. All rights reserved. What is Machine Learning?
- 6. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Machine Learning Process Flow Using the data, the system learns an algorithm, and then uses it to build a predictive model. The system then performs the recommended task and uses feedback data to tune the model to be more accurate.
- 7. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Machine Learning Example Example: Product Recommendation Engine New Input Machine Learning Algorithm Customer Buying History (Data set ) Model training recommendfeed 1 2 3 4
- 8. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Machine Learning Applications
- 9. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Machine Learning Applications Siri RetailHealth Care
- 10. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Types Of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning
- 11. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Supervised Learning Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X) It is called Supervised Learning because the process of an algorithm learning from the training dataset can be thought as a teacher supervising the learning process
- 12. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Supervised Learning Workflow Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value Raw Data Train Labels Feature Extraction Train The Model Model Evaluate New Data Feature Extraction Predict Labels
- 13. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Data Set For Supervised Learning User ID Gender Age Estimated Salary Purchased 15624510 Male 19 19000 0 15810944 Male 35 20000 0 15668575 Female 26 43000 0 15603246 Female 27 57000 0 15804002 Male 19 76000 0 15728773 Male 27 58000 0 15598044 Female 27 84000 0 15694829 Female 32 150000 1 15600575 Male 25 33000 0 15727311 Female 35 65000 0 Label Features
- 14. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Unsupervised Learning ▪ Unsupervised learning is the training of a model using information that is neither classified nor labelled ▪ This model can be used to cluster the input data in classes on the basis of their statistical properties
- 15. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Reinforcement Learning • Reinforcement Learning is learning by interacting with a space or an environment. • An RL agent learns from the consequences of its actions, rather than from being taught explicitly. It selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration). Agent Environment Action at R t+1 S t+1 Reward R t State St
- 16. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Demo - Linear Regression
- 17. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Housing Price Prediction You have been hired by a real-estate company to prepare a model that can predict the housing price in a particular area
- 18. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Housing Price Prediction In order to train the model, we will use Boston dataset. The dataset looks like this: • CRIM - per capita crime rate by town • ZN - proportion of residential land zoned for lots over 25,000 sq.ft. • INDUS - proportion of non-retail business acres per town. • CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) • NOX - nitric oxides concentration (parts per 10 million) • RM - average number of rooms per dwelling • AGE - proportion of owner-occupied units built prior to 1940 • DIS - weighted distances to five Boston employment centres • RAD - index of accessibility to radial highways • TAX - full-value property-tax rate per $10,000 • PTRATIO - pupil-teacher ratio by town • B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town • LSTAT - % lower status of the population • MEDV - Median value of owner-occupied homes in $1000's
- 19. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Linear Regression ➢ Linear Regression Analysis is a powerful technique used for predicting the unknown value of a variable (Dependent Variable) from the known value of another variables (Independent Variable). ▪ A Dependent Variable(DV) is the variable to be predicted or explained in a regression model. ▪ An Independent Variable(IDV) is the variable related to the dependent variable in a regression equation.
- 20. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Linear Regression Dependent variable Y-intercept Slope of the line Independent variable Y = a + bX ➢ Y-intercept (a) is that value of the Dependent Variable(y) when the value of the Independent Variable(x) is zero. It is the point at which the line cuts the y-axis. ➢ Slope (b) is the change in the Dependent Variable for a unit increase in the Independent Variable. It is the tangent of the angle made by the line with the x-axis.
- 21. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Linear Regression Label or dependent variables Features or Independent variables
- 22. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Use-Case Implementation Raw Data Training Data Testing Data Define Features and Labels Train the model Evaluate Model Calculate Accuracy
- 23. Copyright © 2017, edureka and/or its affiliates. All rights reserved. Session In A Minute What is Machine Learning? Types Of Machine Learning Machine Learning Applications Use-Case Linear Regression
- 24. Copyright © 2017, edureka and/or its affiliates. All rights reserved. WebDriver vs. IDE vs. RC ➢ Data Warehouse is like a relational database designed for analytical needs. ➢ It functions on the basis of OLAP (Online Analytical Processing). ➢ It is a central location where consolidated data from multiple locations (databases) are stored.