Support Vector machines are a common **supervised** machine learning algorithm used in both **classification** and **regression** problems, however are most commonly used for classification which will be the focus for this article.

The job of a support vector machine for classification problems is take l**abelled data** such as the following:

and determine a **hyperplane **that separates the data:

Please consider reading Episode 8.1 if you are new to clustering algorithms.

An** explanation** of the Hierarchical clustering algorithm: Episode 8.3

Please consider watching this video if any section of this article is unclear.

How to set up your programming environment can be found at the start of :**Episode 4.3**

You can view and use the **code** and **data** used in this episode here: **Link**

*(We will be working with the same dataset as Ep8.2 but will now work with 3 variables instead of 2)*

Place the following data taken from iris plants into clusters to…

In the previous episode we have taken a look at the popular clustering technique called

K-means clustering. In this episode we will take a look at another widely used clustering technique calledHierarchical clustering.

Please consider watching this video if any section of this article is unclear:

Hierarchical clustering is an **unsupervised machine learning algorithm** where its job is to find clusters within data. We can then use these clusters identified by the algorithm to make predictions for which group or cluster a new observation belongs to.

Similar to K-means clustering, Hierarchical clustering takes data and finds…

An explanation of the K-means clustering algorithm: Episode 8.1

Please consider watching this video if any section of this article is unclear.

How to set up your programming environment can be found at the start of :**Episode 4.3**

You can view and use the **code** and **data** used in this episode here: **Link**

Place the following data taken from iris plants into clusters to see if we can identify different plants given their **petal width **and **sepal length**:

We have taken a look at linear and logistic regression and how to implement both algorithms in Python. These algorithms are examples of supervised machine learning algorithms since they take a final value output. We will now go on to look at our first unsupervised machine learning algorithm for this series.

Please consider watching this video if any section of this article is unclear:

K-means clustering is an** ****unsupervised machine learning algorithm**, where its job is to find clusters within data. …

In this Episode we will be expanding on Logistic Regression in Python, implementing much more data pre-processing steps on a larger data set that contains both **numerical** and** categorical data** (words).

Please consider watching this video if any section of this article is unclear:

Construct a logistic regression model to predict** if it** **will rain tomorrow** in a city in Australia.

Link to data and code can be found in the folder **project 2** here: **Github**

Consider reading Episode 7.1 before continuing, which explains how logistic regression works.

Please consider watching this video if any section of this article is unclear.

How to set up your programming environment can be found at the start of : **Episode 4.3**

You can view and use the **code** and **data** used in this episode here: **Link**

Predict whether it will rain tomorrow in Albury, Australia given the following data:

- We store our data in the variable
**df**short for data frame. **df.shape**gives the number of rows and columns in our data.**df.head**displays the first…

Logistic Regression can be thought of as an

extension of Linear Regression. With Linear Regression our final output for our model took asingle value, however, with logistic regression, we apply an extra function to Linear Regression that puts our finalvalue outputinto a group i.e.1 or 0

Please consider watching this video if any section of this article is unclear.

Logistic regression is a very common **supervised machine learning algorithm **(see Episode 3) used by Data Scientists to categorize data into groups.

The job of **logistic regression** is take a bunch of input data…

This episode combines knowledge from

all previous episodestobuild, evaluate and improvea ridge regression model that makes predictions for weather data in Hungary, Szeged.

**You can view the code used in this Episode here: ****SampleCode**

Construct a **regression model** that makes reasonable predictions for **Humidity **given the follow data:

Our model should take new inputs of: **Temperature, Wind-speed, Pressure e.t.c **and come up with a reasonable estimate for: **Humidity.**

We are going to be using **Jupyter Notebook **and the **Sci-kit learn** library to construct this model. …

So far, when implementing all of our regression models in python, we have been using **all of our data** to construct our model:

This, however, often leads to models which** overfit**** our data** and it becomes very **difficult to evaluate** and **make improvements** to our model.

To address this problem, before creating our model, we split our data into two sections:

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