Machine learning and linear regression programming
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This document provides an overview of machine learning (ML) and its relationship with artificial intelligence (AI), defining key concepts such as supervised, unsupervised, and reinforcement learning. It covers various types of learning models, including regression and classification, and discusses their applications in real-world scenarios like self-driving cars and healthcare diagnostics. Additionally, the document addresses data processing techniques, feature engineering, and performance evaluation metrics for machine learning models.
Machine learning and linear regression programming
- 2. Agenda ďą Overview of AI and ML ďą Terminology awareness ďą Applications in real world ďą Use cases within Nokia ďą Types of Learning ďą Regression ďą Classification ďą Clustering ďą Linear Regression Single Variable with python
- 3. ⢠Arthur Samuel (1959) Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. ⢠Tom Mitchell (1998) A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Machine Learning Definition
- 4. Artificial Intelligence Vs Machine Learning Vs Deep Learning
- 6. Implies huge data volumes that cannot be processed effectively with traditional applications. Big Data processing begins with raw data that is not aggregated and it is often impossible to store such data in the memory of a single computer Is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data. Big Data Data Mining Is an artificial intelligence technique that is broadly used in Data Mining. ML uses a training dataset to build a model that can predict values of target variables. Data Mining uses the predictive force of Machine Learning by applying various ML algorithms on Big data. Machine Learning
- 7. WHAT IS ARTIFICIAL INTELLIGENCE ⢠Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Knowledge Gain Reasoning Problem Solving Learning
- 8. Artificial Intelligence Machine Learning Supervised Unsupervised Reinforcement
- 9. Types of Learning Supervised Learning Unsupervised Learning Reinforcement Learning Target/outcome variable to be predicted from set of predictors is known at training phase. E.g. Regression, Decision Tree, Random Forest, KNN Target/outcome variable to be predicted from set of predictors is unknown at training phase. E.g. Clustering (K- means, Apriori) Machine is trained to take specific decision Exposed to an environment where it trains itself continually using trial and error. E.g. Markov Decision process
- 10. Applications in real world ⢠Google search engine ⢠Self driving cars ⢠Facebook auto tagging ⢠Netflix movie recommendation ⢠Amazon product recommendation ⢠Healthcare diagnosis ⢠Speech recognition ⢠StackOverflow QA tagging ⢠Chatbot
- 11. Data as input (Text files, spreadsheet, SQL database) Feature Engineering (Removing unwanted data, Handle missing values, Normalization or Standardization) Algorithm Output/ Model Pipeline solving ML Problem
- 12. Pipeline in solving ML Problem
- 13. Data Exploration/Feature Engineering 1. Variable Identification ⢠Predictor(s) n Target ⢠Type n Category of variable 2. Univariate Analysis ⢠Central tendency ⢠Measure of Dispersion ⢠Visualization Method ⢠Frequency table(categorical) 3. Bivariate Analysis ⢠Relation between 2 variables ⢠Correlation ⢠Chi-square test ⢠Z-test 4. Missing Value Treatment ⢠Deletion ⢠Imputation ⢠Prediction Model ⢠KNN Imputation 5. Outlier Handling Detection ⢠Very Important to handle outlier ⢠Visualization technique like box- plot, scatter plot, Histogram ⢠Any value beyond -1.5IQR to 1.5IQR is an outlier Treatment ⢠Remove ⢠Scale or Normalize ⢠Transform ⢠Impute
- 14. SUPERVISED LEARNING ⢠Supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input/output pairs. ⢠We build a machine learning model from these input/output pairs, which comprise our training set. ⢠Our goal is to make accurate predictions for new, never-before-seen data. ⢠Supervised learning often requires human effort to build the training set, but afterward automates and often speeds up an otherwise laborious or infeasible task.
- 15. TYPES OF SUPERVISED MODEL ⢠Regression : ⢠regression is the process of predicting a continuous value ⢠Classification ⢠predict a class label, which is a choice from a predefined list of possibilities.
- 16. CLASSIFICATION ⢠Binary Classification : Distinguishing between exactly two classes ⢠Multiclass classification : Classification between more than two classes.
- 17. Types of regression 1. Simple Linear Regression Single predictor + single target y = m*x + c 2. Multiple Linear Regression Multiple predictors + single target y = m1*x1 + m2*x2 + c 3. Polynomial Regression One or many predictors + single target Y = mn * x^n + ⌠+ m2*x^2 + m1*x1 + c 4. Stepwise Regression Useful in case of multiple predictors Add or Remove predictors as needed Forward selection Backward elimination 5. Lasso Regression 6. Ridge Regression 7. ElasticNet Regression
- 18. Simple Linear Regression ⢠Single predictor and single target ⢠Y = b0 + b1*X ⢠Minimum sum squared error ⢠Standard packages are already available ⢠Formula ⢠Programming example
- 19. Classification ďą Type of supervised learning ďą Output or target is a categorical outcome Example ď§ Mail spam or no spam ď§ Weather rainy, sunny, humid ď§ Stock price up or down Predictor(s) Algorithm Categorical Target
- 20. Types of Classification 1. K-nearest Neighbor Classifier 2. Logistic Regression 3. NaĂŻve Bayes 6. Support Vector Machine Classifier 5. Random Forest Classifier 4. Decision Tree Classifier
- 21. Clustering (Unsupervised learning) Cluster 1 Cluster 2 Cluster 3
- 22. Unsupervised learning ⢠Unsupervised learning is the training of machine using information that is neither classified nor labelled For instance, Given an image having both dogs and cats which have not seen ever. Machine tries to find pattern based on shape of head, ears, body structure etc.
- 23. Reinforcement Learning ⢠Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. (source : Wikipedia) Eg : you go near fire , its warm : positive reinforcement you touch fire, it burns your hand : negative reinforcement ď learn not to touch fire ⢠Algorithms for RL include â MonteCarlo methods, Markov Decision Processes, Q- learning etc
- 24. ML in Python: ⢠Numpy ⢠Pandas ⢠Scikit-learn ⢠Matplotlib ⢠Seaborn Non- Programming: ⢠Weka ⢠Orange ⢠RapidMiner ⢠Qlik Sense ⢠xls Deep Learning: ⢠Tensorflow ⢠Keras ⢠PyTorch ⢠Theano Tools And Packages
- 26. LINEAR REGRESSION ⢠Linear regression, or ordinary least squares (OLS), is the simplest and most classic linear method for regression. Linear regression finds the parameters m and b that minimize the mean squared error between predictions and the true regression targets, y, on the training set.
- 27. HOME PRICES area price 2600 550000 3000 565000 3200 610000 3600 680000 4000 725000
- 28. HOME PRICES area price 2600 550000 3000 565000 3200 610000 3600 680000 4000 725000 Given these home prices, find out the price of homes whose area is 3300 square feet 5000 square feet
- 29. SCATTER PLOT
- 30. BEST FIT LINE.
- 31. PREDICT HOME PRICES FOR A GIVEN AREA
- 32. PREDICT HOME PRICES FOR A GIVEN AREA (CONT.)
- 33. PREDICT HOME PRICES FOR A GIVEN AREA (CONT.)
- 34. SLOPE INTERSECTION EQUATION OF A STRAIGHT LINE
- 36. EVALUATING MODEL PERFORMANCE ⢠The performance of a regression model can be understood by knowing the error rate of the predictions made by the model. You can also measure the performance by knowing how well your regression line fit the dataset. ⢠Letâs try to understand how to measure the performance of regression models. ⢠A good regression model is one where the difference between the actual or observed values and predicted values for the selected model is small and unbiased for train, validation and test data sets.
- 37. EVALUATING MODEL PERFORMANCE ⢠To measure the performance of your regression model, some statistical metrics are used. They are- ⢠Mean Absolute Error(MAE) ⢠Root Mean Square Error(RMSE) ⢠Coefficient of determination or R2 ⢠Adjusted R2
- 38. MEAN ABSOLUTE ERROR(MAE) ⢠This is the simplest of all the metrics. It is measured by taking the average of the absolute difference between actual values and the predictions.
- 39. MEAN ABSOLUTE ERROR (MAE)
- 40. ROOT MEAN SQUARE ERROR(RMSE) ⢠The Root Mean Square Error is measured by taking the square root of the average of the squared difference between the prediction and the actual value. ⢠It represents the sample standard deviation of the differences between predicted values and observed values(also called residuals). It is calculated using the following formula:
- 41. ROOT MEAN SQUARE ERROR(RMSE)
- 42. COEFFICIENT OF DETERMINATION OR R^2 ⢠It measures how well the actual outcomes are replicated by the regression line. ⢠It helps you to understand how well the independent variable adjusted with the variance in your model. ⢠That means how good is your model for a dataset. ⢠The mathematical representation for R^2 is Here, SSR = Sum Square of Residuals(the squared difference between the predicted and the average value) SST = Sum Square of Total(the squared difference between the actual and average value)
- 43. COEFFICIENT OF DETERMINATION OR R^2 (CONT.) ⢠Here the green line represents the regression line and the red line represents the average line. The differences in data points from these lines are taken in the equation. ⢠Usually, the value of R^2 lies between 0 to 1(it can be negative if the regression line somehow has a worse fit than the average!). The closer its value to one, the better your model is. This is because either your regression line has well fitted the dataset or the data points are distributed with low variance. Which lessens the value of the Sum of Residuals. Hence, the equation gets closer to one.
- 44. THANK YOU
Editor's Notes
- #16: list of possibilities. classification approach can be thought of as a means of categorizing or "classifying" some unknown items into a discrete set of "classes."
- #30: plt.scatter(df['area'],df['price'] , marker = '*', color = 'red')
- #31: plt.xlabel('area') plt.ylabel('price') plt.scatter(df['area'],df['price'], marker = '*', color = 'red') plt.plot(df['area'], model.predict(df[['area']]))