# Introduction to Machine Learning: ML-Series(1 of 10)

## Machine Learning

Machine Learning helps engineers to make sense of their data. We, software engineers, while programming, think mathematically but Machine Learning lets computer think naturally.

Machine Learning examines huge amounts of data looking for patterns, then it generates code that lets you recognize those patterns in new data. Your applications can use this generated code to make better predictions.This is how machine learning can help you create smarter applications.

Example 1: If I will show 5 labeled pictures of different flowers to a child. Upon showing the pictures again and hiding the names of the flower, the child is able to recall the name because he has memorized it based on the previous knowledge.

Example 2: When you shop online, machine learning helps recommend other products you might like based on what you’ve purchased earlier. When your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud.

Similarly, we can feed huge data sets to the machine and the machine makes predictions for future. Teaching the machine ‘how to learn’ is machine learning.

#### What is Machine Learning?

• Machine Learning allows you to construct and use algorithms those learn from data.
• Forecasts or predictions from machine learning can make apps and devices smarter.
• Using machine learning, computers learn without being explicitly programmed.

What is Azure Machine Learning?

Azure Machine Learning (Azure ML) is a cloud service that helps people execute the machine learning process. As its name suggests, it runs on Microsoft Azure, a public cloud platform. Because of this, it can work with very large amounts of data and be accessed from anywhere in the world. Using Azure ML requires just a web browser and an internet connection.

The machine learning process starts with raw data and ends up with a model derived from that data as shown below.

3. Spot Check Algorithms.
4. Improve Results
5. Present Results.

## Labels

label is a thing we’re predicting—the y variable in the simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.

## Features

feature is an input variable—the x variable in the simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as:

{x1,x2,…xN}

In the spam detector example, the features could include the following:

• words in the email text
• time of day the email was sent
• the email contains the phrase “one weird trick.”

## Examples

An example is a particular instance of data, x. (We put x in boldface to indicate that it is a vector.) We break examples into two categories:

• labeled examples
• unlabeled examples

labeled example includes both feature(s) and the label. That is:

  labeled examples: {features, label}: (x, y)

#### What is a Model in Machine Learning?

Machine Learning applies statistical techniques to large amounts of data, looking for the best pattern to solve your problem. It then generates an implementation—code—that can recognize that pattern. This generated code is referred to as a model, and it can be called by applications that need to solve this problem.

#### What is Training a Model?

Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization.

#### What is a Loss?

The loss is the penalty for a bad prediction. That is, the loss is a number indicating how bad the model’s prediction was on a single example. If the model’s prediction is perfect, the loss is zero; otherwise, the loss is greater. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples.

We can broadly categorize machine learning in following four categories.

Here is even more drilled down version of above diagram.

We will cover each of above box in a detailed post.

Coming Soon (Remaining 9 Blog Posts of this series)