Machine Learning

Components of Machine Learning:

In recent years, the field of machine learning has witnessed a great transformation, with the development of numerous new algorithms. These algorithms though immensely different are a combination of three major components, which together make a machine learning model. These three important components of machine learning are:

1. Representation:

Describes how the knowledge or data is represented. Here, a classifier must be represented in a formal language that can be handled by the computer.

2. Evaluation:

It is how the data/model is judged or preferred over another. Here an evaluation function, which is also known as an objective function or scoring function is used to distinguish suitable classifiers from the bad ones.

3. Optimization:

Estimates model parameters using optimization algorithms, which helps search among the classifiers in the language for the highest-scoring classifier.

From machine learning to data science workflow, these three components are crucial for the implementation of learning algorithms.

Types of Machine Learning Algorithms:

Before we move on to discussing the most common machine learning algorithms used, let us understand the types of machine learning algorithms that form the foundation of machine learning. There are mainly four machine learning techniques, each of which is further divided into different categories. The different types of machine learning algorithms are:

1. Supervised Learning:

The first type of learning algorithm supervised learning trains the program on a predefined set of training examples that facilitate its ability to reach a conclusion. This training process continues until the desired level of accuracy is achieved by the training data. Supervised Machine Learning is further categorized into:

2. Unsupervised Learning:

This type of machine learning algorithm learns from test data that is not labeled, classified or categorized. Moreover, it identifies the commonalities in the data and reacts based on their presence and absence in each new piece of data. It uses two techniques to achieve this:

3. Semi-Supervised Learning:

Semi-supervised learning, as suggested by the name falls between supervised and unsupervised learning. Here, the algorithm is trained upon a combination of labeled and unlabeled data, which produces considerable improvements in learning accuracy.

4. Reinforcement:

This type of machine learning algorithm trains software agents to complete a task using positive and negative reinforcement. It continuously learns from the environment in an iterative fashion and aims at using observations to take actions that would maximize the reward or minimize the risk. There are two types of reinforcement:

5. Self Learning:

A machine learning paradigm, self-learning requires no external rewards and/or teacher advice and is driven by the interaction between cognition and emotion. There is neither a separate reinforcement input nor an advice input from the environment.

6. Feature Learning:

Also known as representation learning algorithm, feature learning transforms the information in the input to make it useful as a pre-processing step before performing classification or predictions.

7. Sparse Dictionary Learning:

It is a feature learning method where training is represented as a linear combination of basis function and is assumed to be a sparse matrix.

8. Anomaly Detection:

Also known as Outlier Detection, anomaly detection identifies rare items, events or observations that raise suspicions by being significantly different from the majority of the data.

These algorithms, though different in their approaches, the type of data they input and output, as well as the type of task they are intended to perform are an integral part of machine learning, as they help make machines capable of performing their intended tasks.

Machine Learning Algorithms:

As stated earlier, researchers and data scientists have created numerous machine learning algorithms, which makes it important for us to identify the once that are most popular and suitable for completing tasks. Some of these popular algorithms for machine learning are:

1. Linear Regression:

A well known and well-understood algorithm, linear regression is a linear model that is used to estimate the real values based on continuous variables. It expresses a linear relationship between the input X and output Y of the algorithm, which is represented as a line in the form of y = a + bx.

2. Logistic Regression:

Unlike linear regression, logistic regression is a classification technique that is used to estimate the discrete values based on a given set of independent variables. It is also known as logit regression, as it predicts the probability of occurrence of an event by fitting data to a logit function.

3. Decision Tree:

Used to classify data, the decision tree uses a tree-like graph to show the predictions that result from a series of feature-based splits. It consists of a series of nodes that start at the base with a single node and extends to various leaf nodes that represent the categories.

4. Naive Bayes:

Another important classification technique, Naive Bayes is a supervised machine learning algorithm that uses Bayes' theorem to make predictions. It assumes that the presence of a particular feature in a class is unrelated to the presence of any other features. Moreover, Naive Bayes is particularly useful for large data sets, as it is easy to build.

5. kNN:

K-Nearest Neighbors or kNN is a machine learning algorithm that can be used to solve issues of classification and regression. It makes predictions using the training data directly, wherein it uses the entire dataset as the training set, rather than splitting the data into training and test set.

6. K-Means:

A simple and popular unsupervised machine learning algorithm, K-Means helps solve clustering problems. It consists of various clusters, with their own centroids. The objective of this algorithm is to identify k number of centroids and then allocates them a data point.

Now that we understand the basics of machine learning algorithms, let’s get a peek at how machine learning works.

How Does Machine Learning Works?

The process of machine learning involves two techniques, Supervised and Unsupervised learning. However, to accomplish a task as well as to make accurate predictions, it follows a set workflow that involves:

1. Gathering Data:

The process of machine learning is initiated by gathering the training data. This is the most important step of the process as the quantity and quality of the data gathered determines the effectiveness of the predictive model.

2. Data Preparation:

After accumulating the training data, it is loaded into a suitable place and prepared for use in machine learning training.

3. Model Selection:

Since there are numerous models created by researchers and data scientists, such as Linear Regression, SVM, Naive Bayes, kNN, K-Means, Random Forest, etc., it is crucial to select a suitable model.

4. Training:

Once the suitable model is selected, the data is used to incrementally improve the model’s ability to perform the necessary prediction. This involves initializing some random values for variables and attempting to predict the output. This is the core of machine learning and it is based on this step that future predictions are made by a machine learning system.

5. Evaluation:

Evaluation is the step where the effectiveness of the model is tested. This allows the model to be tested against data and scenarios that weren’t used in training. Moreover, it helps us see how the model performs in the real world.

6. Hyperparameter Tuning:

Another important step of the workflow, hyperparameter tuning or parameter tuning is used to further improve the training, to ensure the accuracy of the predictions.

7. Prediction:

Finally, the model is used to make predictions.

To ensure accurate implementation of the process, machine learning engineers must possess a few crucial skills like software engineering chops to implement models in practice, knowledge of machine learning theory to determine which model to implement and why, ability to use statistical inference to quickly evaluate whether a model is working or not, domain-level knowledge, as well as the ability to communicate insights.

Applications of Machine Learning:

The impact of machine learning can evidently felt across the spectrum, with industries leveraging its capabilities to create machines and computers with human-like abilities. Therefore, to highlight some of this impact, listed below are a few of these machine learning applications that are acting as a stepping stone for the futuristic tech and helping industries reform today.

1. eCommerce:

Predicting customer churn to product recommendations, predicting fraudulent transactions, and more, machine learning application is making eCommerce more secure and reliable for customers.

2. Finance:

Machine learning is helping finance and banking sector improve transaction security, as well as evaluate the risks on credit offers. Additionally, it is becoming useful in determining where to invest money and who to send credit cards, among other things.

3. Social Networks:

Facebook, Pinterest, Instagram, etc., social media networks are relying on machine learning algorithms to improve insights for users and provide them with content that resonates with their interest.

4. Intelligent Gaming:

The implementation of machine learning in the gaming industry has helped bring improvements in the Graphics Processing Units (GPU) processing speed. Moreover, it is simplifying modeling complex systems and world creation as well as enhancing images and rendering, etc.

5. Virtual Personal Assistants:

From Alexa, Google Now, and Siri to other virtual assistants, machine learning is an important part of virtual personal assistants. It collects and refines information/data based on their previous involvement.

6. Email Spam & Malware Filtering:

Machine Learning is currently playing a key role in filtering spam and malware from the user’s inbox. Gmail filters 99.9% of spam emails through machine learning algorithms.

7. Web Search:

Another area where machine learning has initiated a revolution in the web search. Search engines are using the machine learning algorithms to rank pages based on what users are more likely to click on, making search more convenient and quick.