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. What is Machine Learning? Copyright © 2017, edureka and/or its affiliates. All rights reserved.

4. 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. How to achieve? Machine learning uses data to detect patterns and create a model and adjust program actions accordingly Copyright © 2017, edureka and/or its affiliates. All rights reserved.

5. What is Machine Learning? Copyright © 2017, edureka and/or its affiliates. All rights reserved.

6. 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. Copyright © 2017, edureka and/or its affiliates. All rights reserved.

7. Machine Learning Example Example: Product Recommendation Engine 1 Machine Learning Algorithm Customer Buying History (Data set ) 2 training 4 3 feed recommend New Input Model Copyright © 2017, edureka and/or its affiliates. All rights reserved.

8. Machine Learning Applications Copyright © 2017, edureka and/or its affiliates. All rights reserved.

9. Machine Learning Applications Siri Health Care Retail Copyright © 2017, edureka and/or its affiliates. All rights reserved.

10. Types Of Machine Learning Reinforcement Learning Supervised Learning Unsupervised Learning Copyright © 2017, edureka and/or its affiliates. All rights reserved.

11. 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 Copyright © 2017, edureka and/or its affiliates. All rights reserved.

12. 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 Train The Model Feature Extraction Model Evaluate Raw Data Train Labels Feature Extraction Labels Predict New Data Copyright © 2017, edureka and/or its affiliates. All rights reserved.

13. Data Set For Supervised Learning User ID 15624510 15810944 15668575 15603246 15804002 15728773 15598044 15694829 15600575 15727311 Gender Male Male Female Female Male Male Female Female Male Female Age 19 35 26 27 19 27 27 32 25 35 Estimated Salary 19000 20000 43000 57000 76000 58000 84000 150000 33000 65000 Purchased 0 0 0 0 0 0 0 1 0 0 Features Label Copyright © 2017, edureka and/or its affiliates. All rights reserved.

14. 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 ▪ Copyright © 2017, edureka and/or its affiliates. All rights reserved.

15. 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 Reward R t State St Action at R t+1 Environment S t+1 Copyright © 2017, edureka and/or its affiliates. All rights reserved.

16. Demo - Linear Regression Copyright © 2017, edureka and/or its affiliates. All rights reserved.

17. 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 Copyright © 2017, edureka and/or its affiliates. All rights reserved.

18. 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 • • • • • • • • • • • • • • Copyright © 2017, edureka and/or its affiliates. All rights reserved.

19. 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. ▪ Copyright © 2017, edureka and/or its affiliates. All rights reserved.

20. Linear Regression Independent variable Dependent variable Y = a + bX Y-intercept Slope of the line ➢ 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. Copyright © 2017, edureka and/or its affiliates. All rights reserved.

21. Linear Regression Features or Independent variables Label or dependent variables Copyright © 2017, edureka and/or its affiliates. All rights reserved.

22. Use-Case Implementation Define Features and Labels Training Data Train the model Evaluate Raw Data Model Calculate Accuracy Testing Data Copyright © 2017, edureka and/or its affiliates. All rights reserved.

23. Session In A Minute What is Machine Learning? Machine Learning Applications Types Of Machine Learning Use-Case Linear Regression Copyright © 2017, edureka and/or its affiliates. All rights reserved.

24. 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. Copyright © 2017, edureka and/or its affiliates. All rights reserved.