# AI News, scikit-learn video #5: Choosing a machine learning model

- On Wednesday, March 7, 2018
- By Read More

## scikit-learn video #5: Choosing a machine learning model

Welcome back to my video series on machine learning in Python with scikit-learn.

An overfit model, like the one pictured above, has learned the noise in the data (the green line) rather than the signal (the black line).

If you want to understand this week's material at a deeper level, I strongly recommend that you review the two resources below on the bias-variance tradeoff.

It's a critical topic that shows up throughout machine learning, and will help you to gain an intuitive sense for why models behave the way they do.

In the next video, we'll learn our first technique for modeling regression problems, in which the goal is to predict a continuous response value.

- On Thursday, March 8, 2018
- By Read More

## Predict an answer with a simple model

Learn how to create a simple regression model to predict the price of a diamond in Data Science for Beginners video 4.

I take a notepad and pen into the jewelry store, and I write down the price of all of the diamonds in the case and how much they weigh in carats.

Each column has a different attribute - weight in carats and price - and each row is a single data point that represents a single diamond.

Notice that it meets our criteria for quality: Now we'll pose our question in a sharp way: "How much will it cost to buy a 1.35 carat diamond?"

Our list doesn't have a 1.35 carat diamond in it, so we'll have to use the rest of our data to get an answer to the question.

The range of the weights is 0 to 2, so we'll draw a line that covers that range and put ticks for each half carat.

Next we'll draw a vertical axis to record the price and connect it to the horizontal weight axis.

Data scientists explain this by saying that there's the model - that's the line - and then each dot has some noise or variance associated with it.

There's the underlying perfect relationship, and then there's the gritty, real world that adds noise and uncertainty.

This envelope is called our confidence interval: We're pretty confident that prices fall within this envelope, because in the past most of them have.

Now we can say something about our confidence interval: We can say confidently that the price of a 1.35 carat diamond is about $10,000 - but it might be as low as $8,000 and it might be as high as $12,000.

- On Thursday, February 21, 2019

**Training/Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7**

Welcome to part seven of the Deep Learning with Neural Networks and TensorFlow tutorials. We've been working on attempting to apply our recently-learned basic deep neural network on a dataset...

**Train an Image Classifier with TensorFlow for Poets - Machine Learning Recipes #6**

Monet or Picasso? In this episode, we'll train our own image classifier, using TensorFlow for Poets. Along the way, I'll introduce Deep Learning, and add context and background on why the...

**Training a machine learning model with scikit-learn**

Now that we're familiar with the famous iris dataset, let's actually use a classification model in scikit-learn to predict the species of an iris! We'll learn how the K-nearest neighbors (KNN)...

**Selecting the best model in scikit-learn using cross-validation**

In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validat...

**Model Fitness - Mean Square Error(Test & Train error)**

In this video you will learn how to measure whether the Regression model really fits your data well. You will also learn why to use test error to measure model fitness For all our videos &...

**Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4**

Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit to our liking, and then...

**Data Mining with Weka (2.2: Training and testing)**

Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing Slides (PDF):

**Understanding Machine Learning with Python | Module 5 | Training the Model**

Training the Model Introduction to Training Understanding Machine Learning with Python. In the previous modules, we covered the workflow steps of asking the right question, where we defined...

**How to Make an Image Classifier - Intro to Deep Learning #6**

We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional...

**Capio Speech-Team-as-a-Service (STaaS) Platform Demo**

In the past, customizing large vocabulary speech recognition models to be both fast and highly accurate for specific use cases has required deep technical expertise or an expensive speech team…...