Applications of machine learning in data science

 Regression and classification are of primary importance to a data scientist. To achieve these goals, one of the main tools a data scientist uses is machine learning. The uses for regression and automatic classification are wide ranging, such as the following:

• Finding oil fields, gold mines, or archeological sites based on existing sites (classification and regression)
• Finding place names or persons in text (classification)
• Identifying people based on pictures or voice recordings (classification)
• Recognizing birds based on their whistle (classification)
• Identifying profitable customers (regression and classification)
• Proactively identifying car parts that are likely to fail (regression)
• Identifying tumors and diseases (classification)
• Predicting the amount of money a person will spend on product X (regression)
• Predicting the number of eruptions of a volcano in a period (regression)
• Predicting your company’s yearly revenue (regression)
• Predicting which team will win the Champions League in soccer (classification)

Occasionally data scientists build a model (an abstraction of reality) that provides insight to the underlying processes of a phenomenon. When the goal of a model isn’t prediction but interpretation, it’s called root cause analysis. Here are a few examples:

• Understanding and optimizing a business process, such as determining which products add value to a product line
• Discovering what causes diabetes
• Determining the causes of traffic jams

This list of machine learning applications can only be seen as an appetizer because it’s ubiquitous within data science. Regression and classification are two important techniques, but the repertoire and the applications don’t end, with clustering as one other example of a valuable technique.

What is machine learning and why we should care about

 “Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.”
                                                 —Arthur Samuel, 19591

 

The definition of machine learning coined by Arthur Samuel is often quoted and is genius in its broadness, but it leaves you with the question of how the computer learns. To achieve machine learning, experts develop general-purpose algorithms that can be used on large classes of learning problems. When you want to solve a specific task you only need to feed the algorithm more specific data. In a way, you’re programming by example. In most cases a computer will use data as its source of information and compare its output to a desired output and then correct for it. The more data or “experience” the computer gets, the better it becomes at its designated job, like a human does.

When machine learning is seen as a process, the following definition is insightful:

“Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given.”
                                                —Mike Roberts2
 

For example, as a user writes more text messages on a phone, the phone learns more about the messages’ common vocabulary and can predict (autocomplete) their words faster and more accurately.

In the broader field of science, machine learning is a sub field of artificial intelligence and is closely related to applied mathematics and statistics. All this might sound a bit abstract, but machine learning has many applications in everyday life.

Here are the important points from the above discussion 

1. Defined by Arthur Samuel as a field of study that enables computers to learn without explicit programming.Experts develop general-purpose algorithms for large learning problems.
2. For specific tasks, more specific data is fed to the algorithm.
3. Machines use data as their source of information and compare their output to desired outputs.
4. As a process, machine learning improves accuracy by collecting and learning from given data.
5. Examples include a phone learning about common vocabulary and predicting user's words faster.
6. Machine learning is a sub field of artificial intelligence, closely related to applied mathematics and statistics.

Understanding why data scientists use machine learning

The Role of Machine Learning in Data Science

Data Science is all about generating insights from raw data. This can be achieved by exploring data at a very granular level and understanding the trends. Machine learning finds hidden patterns in the data and generates insights that help organizations solve the problem.

The role of Machine learning in Data Science comes into play when we want to make accurate estimates about a given set of data, such as predicting whether a patient has cancer or not.

The role of machine learning in Data Science occurs in 9 steps:

1. Understanding the Business Problem

To build a successful business model, it’s very important to understand the business problem that the client is facing. Suppose the client wants to predict whether the patient has cancer or not. In such scenario, domain experts understand the underlying problems that are present in the system.
 

2. Data Collection

After understanding the problem statement, you have to collect relevant data. As per the business problem, machine learning helps collect and analyze structured, unstructured, and semi-structured data from any database across systems. 

3. Data Preparation

The first step of data preparation is data cleaning. It is an essential step for preparing the data. In data preparation, you eliminate duplicates and null values, inconsistent data types, invalid entries, missing data, and improper formatting. 

4. Exploratory Data Analysis (EDA)

Exploratory Data Analysis lets you uncover valuable insights that will be useful in the next phase of the Data Science lifecycle. EDA is important because, through EDA, you can find outliers, anomalies, and trends in the dataset. These insights can be helpful in identifying the optimal number of features to be used for model building. 

5. Feature Engineering

Feature engineering is one of the important steps in a Data Science Project. It helps in creating new features, transforming and scaling the features. In this domain, expertise plays a key role in generating new insights from the data exploration step. 

6. Model Training

In Model training, we fit the training data; this is where “learning” starts. We train the model on training data and test the performance on testing data i.e., unseen data. 

7. Model Evaluation

Once Model Training is done, it’s time to evaluate its performance. So, evaluating your Model on a new dataset will give you an idea of how your Model is going to perform in future data. 

8. Hyperparameter Tuning

After the Model is trained and evaluated, the performance of the Model can be again improved by tuning its parameter. Hyperparameter tuning of the model is important to improve the overall performance of the model. 

9. Making Predictions and Ready to be Deployed

This is the final stage of machine learning. Here, the machine answers each of your questions by its learning. After making accurate predictions, the Data Model is deployed into production.

Data scientists use machine learning for a variety of reasons, but here are some of the most important ones:

1. To extract insights from large datasets: Machine learning algorithms can analyze massive amounts of data much faster and more efficiently than humans can. This allows data scientists to discover hidden patterns, trends, and relationships that might otherwise go unnoticed. These insights can be used to inform business decisions, improve product development, personalize customer experiences, and much more.

2. To make predictions: Machine learning models can be trained to learn from historical data and then use that knowledge to make predictions about the future. This can be useful for tasks like forecasting sales, predicting customer churn, or identifying potential fraud.

3. To automate tasks: Machine learning can automate many repetitive and time-consuming tasks that data scientists would otherwise have to do manually. This frees up their time to focus on more strategic work, such as interpreting results and communicating insights to stakeholders.

4. To handle complex data: Machine learning can be used to analyze complex and unstructured data, such as text, images, and audio. This type of data can be difficult to analyze using traditional methods, but machine learning algorithms are able to extract valuable insights from it.

5. To improve accuracy and efficiency: Machine learning models can often achieve higher accuracy and efficiency than traditional data analysis methods. This is because they can learn and improve over time, as they are exposed to more data.