# Course Notes - Google ML Intro ### Course Details Title: **Introduction to Machine Learning** Link: ### Introduction Peter Norvig, Director of Research at Google gives the intro explaining how ML helps software engineers - reduce the time programming, customize products for specific people, complete seemingly impossible tasks. Also, philosophical reason - it changes the way you think about a problem. Focus shifts from mathematical science to a natural science, running experiments, use statistics instead of logic to analyze the results. ### Tools Collaboratory - Free Jupiter notebook environment that runs fully in the cloud. Pandas - Python library for data structures and data analysis. It uses **DataFrames** and **Series**. It can load an entire file into a DataFrame, show interesting statistics about data, plot a histogram etc., More info at . You can run code in this environment and see the results in real-time. ### Framing **Supervised ML** - System that combines inputs to provide useful predictions on data that was never seen before. * **Label** - target that we are predicting; eg: "spam", "not spam". It is the **y** variable in linear regression. * **Feature** - input variable that describes the data (eg: words in the email, to/cc fields, email headers). It is the **x** variable in simple linear regression. * **Example** - A particular instance of data. It can be **labeled** (includes the feature/s and the label) or **unlabeled** (contains feature, but no label). We train the models using labeled examples and then use that model to predict the label of unlabeled examples. * **Model** - defines the relationship between features and labels. eg: spam detection model may identify some features as strongly associated with spam. Models have two phased - Training (creating/learning the model) and Inference (apply the trained model to unlabeled examples). * Types of models * Regression model: predicts continuous value (price of house in CA) * Classification model: predicts discrete values (is the email spam? is this an image of dog/cat/lion) ### Descending into ML * Linear Regression - method of finding a straight line or hyperplane that fits a set of points in the best way. **y = wx + b** (w is the weight and b is the bias) * Loss - penalty of bad prediction. Number that indicates how bad the prediction was on a single example. * Goal of training a model is to find a set of weights and biases that have *low* loss *on average* across all examples. * One of the methods to represent Loss is: * **L2 Loss (**aka **Squared Loss):** the square of the difference b/w predicted value and observed value of label. MSE = Mean Squared Error; D = data set that contains N pairs of examples (x, y) $$ MSE = \frac{1}{N}\sum\_{(x,y) \epsilon D} (y - prediction(x))^2 $$ --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://kb.annjose.com/gen-ai-ml/notes-from-google-ml-course.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.