Introduction to Machine Learning
- 1. Introduction to Machine Learning 15 Slides
- 2. Definition • The art of making machines intelligent without explicit programming Machine Learning is a field which consists of learning algorithms or techniques which • Execute some tasks T (Regression/Classification) • Improve their performance P (model performance) • With experience E (Delta) Algorithms that can learn from observational data, and can make predictions based on it.
- 3. 3 Stages of ML process • Representation (Selection of an algorithm/parameters) • Evaluation (Objective function) • Optimization (finding the optimal parameters)
- 4. Types of Machine Learning Supervised Machine Learning • Training is done using labelled data • Algorithm learns the mapping function from the input to the output. Y = f(X) • Examples: • Regression – used to predict continuous values • Classification – used to predict categorical values • In supervised learning, the algorithm learns from “correct” answers. • The model is created and then used to predict answers from future data • Example: Train a model to predict car price based on car attributes using historical sales data. The model can predict optimal price for the new car that haven’t been sold before. Movie Genres
- 5. Types of Machine Learning Unsupervised Machine Learning • Training is done using unlabeled data • Algorithms are left to their own devices to discover and present the interesting structure in the data. • Clustering – used to discover the inherent groupings in the data • Association – used to discover rules that describe large portions of the data • Example: group (cluster) of objects into 2 different sets based on characteristics of those objects. Dating Sites
- 6. Evaluating Supervised Learning • If you have a set of training data that includes the value you are trying to predict; you don’t have to guess if the resulting model is good or bad. • If you have enough training data, you can split into two parts: a training set and a test set. • Then you train the model using training set. Then measure the model’s accuracy by asking it to predict values for the test set, and compare that to the unknown, true values.
- 7. Supervised ML - Regression Regression is a technique that determines the relationship between one or more independent variables and a dependent variable Other naming style Dependent : Independent Target : Input Criterion : Predictor
- 8. Linear Regression • Simple Linear Regression : Only one independent variable • Multiple Linear Regression : Two or more independent variables Non-Linear Regression • Dependent or non-linear transformation of independent variables Supervised ML - Regression
- 9. Simple Linear Regression In a Simple Linear Regression, we fit the best line between the dependent variable and the independent variable given as y = mx + c m = Co-efficient of x (i.e. change in y divided by change in x) c = Intercept (represents the variability in y) • Fit a line to a dataset of observations • Use this line to predict unobserved values
- 10. Real life scenarios will definitely have many more independent variables than just one. To model an equation that studies the relationship between the dependent variable and multiple independent variables. The representation equation can be extended to Y = m0 + m1x1 + m2x2 + m3x3 + … + mnxn Where, m0 is the intercept m1 is the coefficient of variable x1 M2 is the coefficient of variable x2 And so on… Multiple Linear Regression
- 11. Logistic Regression is a technique that determines the relationship between a dependent variable and one or more independent variables, with the type of dependent being a dichotomous categorical variable. To maximize or minimize a function, we differentiate the function and find the point when the gradient is zero. Since, this is a non-linear function, we use the gradient descent i.e. calculate the gradient of the function at each i.e. update the values of parameters Supervised ML - Classification
- 12. The key objective in clustering is to identify distinct groups/clusters based on similarities within a given dataset. Agglomerative (Hierarchical) Divisive (K-Means) K-Means Clustering • Start with random point initialization of the required number of centers. (“K”in K- means stands for the number of clusters) • Assign each data point to the “center“ closest to it. (Distance metric = Normal Euclidean distance) • Recalculate centers by averaging the dimensions of the points belonging to each cluster. • Repeat with new centers until we reach a point where the assignments become stable. Unsupervised ML - Classification
- 13. K-Means Clustering • Start with random point initialization of the required number of centers. (“K”in K-means stands for the number of clusters) • Assign each data point to the “center“ closest to it. (Distance metric = Normal Euclidean distance) • Recalculate centers by averaging the dimensions of the points belonging to each cluster. • Repeat with new centers until we reach a point where the assignments become stable. Hierarchical Clustering • Start with “n”clusters (n= # of data points) • Combine the 2 closest clusters • Repeat till only 1 cluster exists. Unsupervised ML - Classification
- 14. Evaluating ML models for Regression
- 15. Evaluating ML models for Regression
- 16. Thank you