---
# System prepended metadata

title: '<center><i class="fa fa-edit"></i> Machine Learning: Framing and Descending into ML </center>'
tags: [Internship]

---

# <center><i class="fa fa-edit"></i> Machine Learning: Framing and Descending into ML </center>
###### tags: `Internship`
:::info
**Goal:**
- [x] Framing
- [x] Descending into ML

**Resources:**
[Framing](https://developers.google.com/machine-learning/crash-course/framing/video-lecture)
[Descending Into ML](https://developers.google.com/machine-learning/crash-course/descending-into-ml/video-lecture)
[Machine Learning](https://hackmd.io/@Derni/HJQkjlnIP)

:::

### Framing 
- Supervised ML
    - Goal: learn to combine inputs to produce predictions on new data
    - Label: variable to predict
        - Represented by y
    - Features: input variables to describe data
        - Represented by {x1, x2,…,xn}
    - Example: particular instance of data, x
    - Labeled example: {features, label} like {x,y}
        - Used to train model
    - Unlabeled example:: {features, ?} like {x, ?}
        - Makes predictions
    - Model: maps examples to predicted labels y’
        - Defined by learned internal parameters
- Regression model: predicts continuous values
- Classification: predicts discrete values
    - Ex: spam, cat or dog

### Descending into ML
- Linear regression: approximate linear relationship with y = b + w1x1
    - y’: predicted label
    - b: bias (y-intercept), also w0
    - w1: weight of feature 1
        - Weight is similar to slope
- More sophisticated models with multiple features can be: y = b+ w1x1 + w2x2 + w3x3
![](https://i.imgur.com/INeIrw1.png)
- Training: learning good values for weights and bias
- Loss: how bad model’s prediction was on a single example
    - Empirical risk minimization: minimize loss
![](https://i.imgur.com/9ZBnXbq.png)
- Squared loss function: “squared error.” For regression
    - Square of difference between prediction and label = (y - y’)^2
    - For Loss on data set, sum all individual L2
    - Mean square error (MSE): Can average squared loss over whole dataset
![](https://i.imgur.com/FuIMqHm.png)
![](https://i.imgur.com/DIq86kn.png)