category: Machine Learning | Python Tutorial

Category: Machine Learning

How do I learn Machine Learning?

It is well known that machine learning represents a complex topic to deal with due to the fact that there are available a multitude of resources that happen to age at a rapid rate.

Moreover, the technical jargon makes learning machine even more tricky and the amount of time you need to allocate for this task is huge.

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Machine Learning

The first approach should be discovering the variety of languages which give capabilities to machine learning.

At this time R and Python represent the most used languages in this field and they feature a vast support from communities worldwide.

Before starting machine learning is highly recommended to study one of those two languages.

Also, you can take into account other languages such as Scala, depending on your needs. But just knowing Python is enough.

Learn Machine Learning

Don`t get overwhelmed by the amount of data you will encounter. It is rather normal to not remember everything and to re-read concepts as you face problems.

  • Step 1:
    Basic Statistics will help you understand how a machine language functions and its statistical libraries or methods. For Python, there are machine learning libraries like sklearn.
import sklearn
  • Step 2:
    Data Exploration. This makes a huge difference between an expert machine learning professional and an average one.

    Learn about feature engineering, outlier treatment or variable identification are all helpful in establishing a qualitative data cleaning in any machine learning language.

    classification

  • Step 3:
    Opt for a learning course such as
    Machine Learning A-Z™: Hands-On Python & R In Data Science.

    This type of courses will offer you a broad introduction in machine learning along with different resources for properly understanding algorithms and techniques.

  • Step 4:
    Advanced machine learning and it can only be done with the knowledge base you create by following closely the first three steps.

    Various concepts like Deep Learning and Machine Learning with Big Data need thorough understanding in order to successfully master machine learning.

Practice makes perfect

Furthermore, it is best to take into account the following:

  • don`t forget to ask yourself why any time you got the chance. This will permit you to see the bigger picture and understand the dataset you have properly.

  • Remember that learning machine language can be done only with a lot of practice.

Bottom line, if you undergo all the steps mentioned above, you will definitely end up being great at applied machine learning.

It is a rather interesting field because of the fact that it is a challenging domain that require constant learning, computer vision and natural machine language understanding.

To become the best machine learning engineer you will have to constantly learn and boost your skills.

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What is Machine Learning?

Machine learning is the field of study that offers computers the ability to learn without being specifically programmed to do so.

It’s a sub-area of artificial intelligence that permits computers to emerge into a self-learning mode. Thus, when faced to new data sets, these computer programs are enabled to learn, grow and develop by themselves.

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Machine Learning Types

Even though the concept of machine learning is used from a long time ago, the capability to autonomously conduct complex mathematical considerations to big data sets it is a recent fact that was achieved in this field a couple of years back.

Also, there are different types of machine learning, along with specific algorithms that properly work with a certain type of machine learning.

  • Supervised machine learning

    Supervised deals with simple tasks, for which recognition algorithms are employed.

    These tasks can only be done if the computer has access to an already established data of input-output pairs.

    If algorithm uses training data, it’s a supervised learning algorithm.

    iris = datasets.load_iris() 
    X = iris.data[:, :2]
    Y = iris.target
  • Unsupervised machine learning algorithms

    Seek patterns from a dataset of correct answers, being useful for exploratory analysis.

    If algorithm does not use training data, but directly runs on the data set, it’s an unsupervised algorithm

    X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4]])
    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
    kmeans.predict([[0, 0], [12, 3]])

    Frequently used algorithms for this type of learning are k-means clustering, main component analysis or autoencoders.

  • Reinforcement learning

    offers to the machine a method to measure its performance by using positive reinforcement.

    This is quite similar to the manner in which humans or animals learn certain tasks due to the fact that the machine tries different ways of solving a problem and it gets rewarded with a signal if it is successful.

Applications

Machine learning applications are vast and applicable to different domains.

  • Data security is one of the domains in which machine learning is more than useful due to the fact that malware is not a problem that will simply disappear. Usually, when it comes to identifying a malware, machine learning is employed for identifying patterns and signal abnormalities.

  • Other uses for machine learning algorithms can be observed in the field of financial trading. Patterns and predictions are those that keep stock markets in the game, which signifies that the algorithms used for machine learning aid in predicting and executing transactions.

  • Marketing personalization also uses machine learning algorithms in order to establish a targeted customer experience depending on their behaviour or location-based data.

  • What is even more interesting about machine learning algorithms, is the fact that ML can be used in health care, too.

    Many scientists and researchers established different patterns in order to train machine for detecting cancer by simply observing cell images. This can be achieved with a vast amount of great quality image data and machine learning algorithms to predict the possibility of a patient getting cancer.

Hence, machine learning is a process that continuously evolves and changes, which might turn out to be more than helpful for our everyday tasks. ML represents a tool that effectively automates the process of analytical model building and permits machines to adapt to new set-ups independently.

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Machine Learning

What is Machine Learning?
The word ‘Machine’ in Machine Learning means computer, as you would expect. So how does a machine learn?

Given data, we can do all kind of magic with statistics: so can computer algorithms.

These algorithms can solve problems including prediction, classification and clustering. A machine learning algorithm will learn from new data.

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Types of learning

There are two types of learning: supervised learning and unsupervised learning.. Say what?

Supervised learning

Let’s suppose we have consumer data. I tell the computer: these customers have a high income, those customers have median income. The training phase.
Then we can ask this computer:

 
You: Does this customer have a high or median income?
Computer: Based on the training data, I predict a high income.

Python code

So does training data have to be large and complex? No, this is also works for small data sets.

Take for instance, this set:

x = [[2, 0], [1, 1], [2, 3]]
y = [0, 0, 1]

So what does this mean:

  • Look at y, there are two possible outputs. Either it’s class 0 or class 1.
  • Then x are the measurements.

The trainining data (x,y) is then used to feed the algorithm, with the feed() method.
n

your_amazing_algorithm.fit(x, y)

Then if you have new measurements, you can predict the output (class 0 or class 1).

print (clf.predict([[2,0]]))

Unsupervised learning

With unsupervised learning algorithms, you have no idea. You give the data to the computer and expect answers. Surprisingly, these work rather well.

If you have data points x, where each value of x is a two dimensional point.
Want to make predictions?

Load an algorithm:

kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

Predict:

kmeans.predict([[12, 3]])

Yes, it can be that easy.

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Machine Learning Tasks

All of the Machine Learning algorithms take data as input, but what they want to achieve is different.

They can be broadly be classified in a few groups based on the task they are designed to solve. These tasks are: classification, regression and clustering.

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Classification

If we have data, say pictures of animals, we can classify them. This animal is a cat, that animal is a dog and so on.

A computer can do the same task using a Machine Learning algorithm that’s designed for the classification task. In the real world, this is used for tasks like voice classification and object detection.

This is a supervised learning task, we give training data to teach the algorithm the classes they belong to.

classification

Regression

Sometimes you want to predict values. What are the sales next month? What is the salary for a job? Those type of problems are regression problems.

The aim is to predict the value of a continous response variable. This is also a supervised learning task

regression

Clustering

Clustering starts with data points. These data points can be measurements like length and width. These can be plotted, each record as a point (length,width).

data points

Clustering is to create groups of data called clusters. Observations are assigned to a group based on the algorithm. This is an unsupervised learning task, clustering happens fully automatically.

clustering

Imagining have a bunch of documents on your computer, the computer will organize them in clusters based on their content automatically.

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The importance of unsupervised learning

With the advent of machine learning and artificial intelligence, machines are getting more and more advanced and their abilities are frequently pushed to the limit.

Nowhere is this more tested than in unsupervised learning, which is a format of learning that a machine uses without any form of training data or guidance.

This form of learning as been more closely associated with true artificial intelligence. Whereas supervised learning may be more popular and common, I will highlight benefits and categories of unsupervised learning in this article.

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Why Unsupervised Learning?

The number one advantage of unsupervised learning is the ability for a machine to tackle problems that humans might find insurmountable either due to a limited capacity or a bias.

Unsupervised learning is ideal for exploring raw and unknown data. It works for a data scientist that does not necessarily know what he or she is looking for.

unknown data

When presented with data, an unsupervised machine will search for similarities between the data namely images and separate them into individual groups, attaching its own labels onto each group.

clustering

This kind of algorithmic behavior is very useful when it comes to segmenting customers as it can easily separate data into groups without any form of bias that might hinder a human due to pre-existing knowledge about the nature of the data on the customers.

Compared to human intelligence

Additionally, unsupervised learning is closer to human cognitive functions as just like a human brain, it deduces patterns from around the world and slowly learns more about the world over time.

A good example would be a child that is been introduced to the world for the first time. Let’s imagine it sees a two-legged creature and hears someone call that creature a chicken, the next couple of two legged creatures it sees whether they be ducks, turkeys or geese would register as chickens to the child.

However, over a period of time, after the child consumes more information on other two-legged creatures like ducks, turkeys or geese, it slowly begins to discern which two-legged creature is which without any external supervision.

Given data x, the unsupervised learning algorithm kmeans trains itself.

kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

This is an example of how unsupervised learning works, furthermore it can be placed under four categories which are clustering, descending dimensions, association and recommendation systems, and reinforcement learning.

Clustering

Clustering is used for significantly reducing large data into simplified forms of information that can be easily digested.

LengthBreathHeightClass
10010020Football Field
552Room
962Room

Descending dimensions are useful in reducing the time taken for a computer to process information.

By merging certain fundamental dimensions for faster results like in a three-dimension system of length, breath and height, the computer can make it a two-dimension system by merging the length and breath into area.

AreaHeightClass
1000020Football Field
252Room
542Room

Recommender systems

Association and recommendation systems operate by collating historical data on a person and suggesting recommendations based on their past viewership and even social media relationships.

This is prevalent in Netflix recommendation tabs, Facebook friend suggestions, Youtube Videos and many more.

Reinforcement learning

Reinforcement learning is a way of making your computer learn by experience through rigorous tactics of trial and error.

All these points highlight the importance of unsupervised learning and showcases their various applications.

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