Machine Learning is a fascinating study. If you are a beginner or simply curious about machine learning, this article covers the basics for you...
Hey, it's DANIYAL SHAIKH. Today I'm gone a show you the real concept of machine learning. As per I think You have heard about the term Machine learning but didn't clear the concept behind it..so this article covers the basics behind it ...
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.. or it can be more clear as a Machine learning is a set of methods by which
computers make decisions autonomously. Using
certain techniques, computers make decisions by
considering or detecting patterns in past records and then
predicting future occurrences. Different types of predictions
are possible, such as weather conditions and house
prices. Apart from predictions, machines have learned how
to recognize faces in photographs and even filter out email
spam. Google, Yahoo, etc, use machine learning to detect
spam emails. Machine learning is widely implemented across
all types of industries. If programming is used to achieve
automation, then we can say that machine learning is used to
automate the process of automation.
In traditional programming, we use data and programs
on computers to produce the output, whereas, in machine
learning, data and output are run on the computer to produce a
program. We can compare machine learning with farming or
gardening,
where seeds --> algorithms, nutrients --> data, and
the gardener and plants --> programs.
We can say machine learning enables computers to learn
to perform tasks even though they have not been explicitly
programmed to do so. Machine learning systems crawl
through the data to find the patterns and when found, adjust
the program’s actions accordingly. With the help of pattern
recognition and computational learning theory, one can study
and develop algorithms (which can be built by learning
from the sets of available data), on the basis of which the computer takes decisions. These algorithms are driven by building a model from sample records. These models are
used in developing decision trees, through which the system
takes all the decisions.
Machine learning programs are also
structured in such a way that when exposed to new data,
they learn and improve over time.
Implementing Machine Learning:
A technique for implementing machine learning is simply be known as Deep Learning.
The concept of Deep Learning I'll show you in my next article just connect with me on LinkedIn. Before we understand how machine learning is implemented
in real life, let’s look at how machines are taught. The
process of teaching machines is divided into three steps.
- Data Input: Text files, spreadsheets or SQL databases are fed as input to machines. This is called the training data for a machine.
- Data Abstraction: Data is structured using algorithms to represent it in simpler and more logical formats. Elementary learning is performed in this phase.
- Generalization: An abstract of the data is used as input to develop the insights. Practical application happens at this stage.
- How well the generalization of abstraction data happens.
- The accuracy of machines when translating their learning into practical usage for predicting the future set of actions.
In this process, every stage helps to construct a better
version of the machine.
Now let’s look at how we utilize the machine in real life.
Before letting a machine perform any unsupervised task, the
five steps listed below need to be followed
- Collecting Data: Data plays a vital role in the machine learning process. It can be from various sources and formats like Excel, Access, Text files, etc. The higher the quality and quantity of the data, the better the machine learns. This is the base for future learning.
- Preparing Data: After collecting data, its quality must be checked and unnecessary noise and disturbances that are not of interest should be eliminated from the data. We need to take steps to fix issues such as missing data and the treatment of outliers.
- Training Model: The appropriate algorithm is selected in this step and the data is represented in the form of a model. The cleaned data is divided into training data and testing data. The training data is used to develop the data model, while the testing data is used as a reference to ensure that the model has been trained well to produce accurate results.
- Model Evaluation: In this step, the accuracy and precision of the chosen algorithm are ensured based on the results obtained using the test data. This step is used to evaluate the choice of the algorithm.
- Performance Improvement: If the results are not satisfactory, then a different model can be chosen to implement the same or more variables are introduced to increase efficiency.
Types of Machine Learning Algorithm:
Machine learning algorithms have been classified into three major categories:
- Supervised Learning: Supervised learning is the most commonly used. In this type of learning, algorithms produce a function which predicts the future outcome based on the input given (historical data). The name itself suggests that it generates output in a supervised fashion. So these predictive models are given instructions on what needs to be learned and how it is to be learned. Until the model achieves some acceptable level of efficiency or accuracy, it iterates over the training data. To illustrate this method, we can use the algorithm for sorting apples and mangoes from a basket full of fruits.
- Unsupervised Learning:
The objective of unsupervised learning algorithms is to represent the hidden structure of the data set in order to learn more about the data. Here, we only have input data with no corresponding output variables. Unsupervised learning algorithms develop the descriptive models, which approach the problems irrespective of the knowledge of the results. So it is left to the system to find out the pattern in the available inputs, in order to discover and predict the output. From many possible hypotheses, the optimal one is used to find the output. Sorting apples and mangoes from a basket full of fruits can be done using unsupervised learning too. But this time the machine is not aware of the differentiating features of the fruits such as color, shape, size, etc. We need to find similar features of the fruits and sort them accordingly. Some of the algorithms we can use here are the K-means clustering algorithm and hierarchical clustering.
- Reinforcement Learning:
In this learning method, ideas and experiences supplement each other and are also linked with each other. Here, the machine trains itself based on the experiences it has had and applies that knowledge to solving problems. This saves a lot of time, as very little human interaction is required in this type of learning. It is also called the trial-error or association analysis technique, whereby the machine learns from its past experiences and applies its best knowledge to make decisions. For example, a doctor with many years of experience links a patient’s symptoms to the illness based on that experience. So whenever a new patient comes, he uses his experience to diagnose the illness of the patient. Some of the algorithms we can use here are the Apriori algorithm and the Markov decision process.
If you want the real concept behind the Evaluation of Machine Learning, then click on the shaded link as given on:
Evaluation of Machine Learning
(These will clear you the types of Machine Learning Algorithms)
Machine Learning Applications:
Machine learning has ample applications in practically every domain. Some major domains in which it plays a vital role are shown in Figure.- Banking and Financial Services: Machine learning plays an important role in identifying customers for credit card offers. It also evaluates the risks involved with those offers. And it can even predict which customers are most likely to be defaulters in repaying loans or credit card bills.
- Healthcare: Machine learning is used to diagnose fatal illnesses from the symptoms of patients, by comparing them with the history of patients with a similar medical history.
- Retail: Machine learning helps to spot the products that sell. It can differentiate between the fast selling products and the rest. That analysis helps retailers to increase or decrease the stocks of their products. It can also be used to recognize which product combinations can work wonders. Amazon, Flipkart, and Walmart all use machine learning to generate more business.
- Publishing and Social Media: Some publishing firms use machine learning to address the queries and retrieve documents for their users based on their requirements and preferences. Machine learning is also used to narrow down search results and news feeds. Google and Facebook are the best examples of companies that use machine learning. Facebook also uses machine learning to suggest to friends.
- Games: Machine learning helps to formulate strategies for a game that requires the internal decision tree style of thinking and effective situational awareness. For example, we can build intelligence bots that learn as they play computer games.
- Face Detection/Recognition: The most common example of face detection is this feature being widely available in smartphone cameras. Facial recognition has even evolved to the extent that the camera can figure out when to click – for instance, only when there is a smile on the face being photographed. Face recognition is used on Facebook to automatically tag people in photos. It’s machine learning that has taught systems to detect a particular individual from a group photo.
- Genetics: Machine learning helps to identify the genes associated with any particular disease.
Advantages and Challenges:
The Advantages of Machine Learning are:
- Machine learning helps the system to decode based on the training data provided in the dynamic or undermined state.
- It can handle multi-dimensional, multi-variety data, and can extract implicit relationships within large data sets in a dynamic, complex and chaotic environment.
- It saves a lot of time by tweaking, adding, or dropping different aspects of an algorithm to better structure the data.
- It also uses continuous quality improvement for any large or complex process.
- There are multiple iterations that are done to deliver the highest level of accuracy in the final model.
- Machine learning allows easy application and comfortable adjustment of parameters to improve classification performance.
The Challenges of Machine Learning are as follows:
- A common challenge is the collection of relevant data. Once the data is available, it has to be pre-processed depending on the requirements of the specific algorithm used, which has a serious effect on the final results.
- Machine learning techniques are such that it is difficult to optimize non-differentiable, discontinuous loss functions. Discontinuous loss functions are important in cases such as sparse representations. Non-differentiable loss functions are approximated by smooth loss functions without much loss in sparsity.
- It is not guaranteed that machine learning algorithms will always work in every possible case. It requires some awareness about the problem and also some experience in choosing the right machine learning algorithm.
- Collection of such large amounts of data can sometimes be an unmanageable and unwieldy task.
Implications for Businesses:
Today machine learning is being used in a number of areas. Google’s self-driving car was developed using machine learning and today machines can lip read faster than humans. ML has been infiltrating almost every sector of finance in recent years. For instance, ML is being used for algorithmic trading, analyzing time series data, portfolio management, fraud detection, customer service, news analysis, investment strategy construction, etc.
But the real power of machine learning is unleashed with neural networks. In the next post, we will discuss a bit more about it.
Languages that Support Machine Learning:
The languages are given below support the implementation of the machine language:
- MATLAB
- R
- Python
- Java