Role of Machine Learning in Data Science Applications

Introduction to Machine Learning and its affects in Data Science domain

Role of machine learning in data science applications
Machine Learning in Data Science Applications

Machine learning is one of the important areas of artificial intelligence, which helps computers get into a self-learning mode without the need for any programming. The computers understand, grow and develop by themselves when they are fed with new data. While machine learning languages are very popular, the application of mathematical calculations with big data is only gaining prominence now. Machine learning finds its use at several platforms, including online recommendation engines, self-driving cars, online fraud detection, etc.

In the recent past, machine learning has witnessed a lot of changes and it has become much more important as it has evolved. Data scientists especially find machine learning very useful as it helps them make important predictions for better decision-making in real-time without the need for any human intervention. As technology, the uses of machine learning makes the life of data scientists easier by analyzing large chunks of data and helping them automate their tasks. With an automated set of generic methods, machine learning is redefining the ways data extraction and interpretation work.

The machine learning involves several processes that are listed below:

  1. Collection of data : The quality and authenticity of the data is an accurate representation of the nature of your machine learning model. Further training is done based on the results of this step.
  2. Preparation of data : The data that is assembled is used for the preparation of training. The data needs to be rectified for removing different types of errors, normalization process, data type conversions, etc.
  3. Training the model : The training process involves answering questions or making correct predictions.
  4. Evaluating the model : The performance evaluation of the model can be done using different kinds of metrics or a combination of metrics. The model then can be tested based on the previous data.
  5. Tuning of parameters : Tuning of the model’s parameters is important to improve the overall performance of the model.
  6. Making predictions : Predictions about the model in real-time can be made with the help of the data that was collected, analysed and tested.
Concept of Data Science

Data Science is a concept in which data scientists use many resources to get new and crucial information from the collected data. The scientists make use of statistics, use their domain expertise and hacking skills to gather huge chunks of data from multiple sources. The gathered data is then combined with predictive analytics and sentiment analysis of machine learning to extract important data. The data is shared with the audience based on their requirements.

An example of data science could be the behavior pattern of your internet surfing and your browsing history based on which you will see a certain type of advertisements when browsing the Internet. The data generated through your browsing history will be used to understand your behavior and you will be shown only those advertisements that will interest you.

The process of data science can be defined in three aspects which include Data Collection, Data Modeling and Analysis, and Problem Solving and Decision Support. During the whole process, data scientists go through different phases of the process which can be defined as below:

  1. Asking questions : Data scientists must consider the following questions: whether there is any business goal to achieve, objects of scientific interest that can be helpful in the future, and the parameters that the ideal answer would fulfill.
  2. Designing data collection process : Once the problem is explained, it will also require data to solve the problem. This process involves thinking about data requirements and the ways to get that. It could involve the purchase of either external data sets or querying internal databases.
  3. Processing the data for analysis : The gathered data is required to be processed first before it can be analyzed. The data that is not maintained properly cannot be accurate. The analysis can get corrupted if there are a lot of errors with the data. It requires a thorough analysis to find accurate insights. Some of the common errors that can show up during the processing stage would be missing values, time zone differences, invalid entries, date range errors, etc.
  4. Exploring the data : The data can be explored once it is processed and cleaned. The gathered data can reveal a lot of insights which can help you discover interesting patterns and trends. These patterns and trends can be analyzed in deep to gain valuable information out of them.
  5. Visualizing and communicating the results : This is the final step of the process in which results need to be visualized and communicated to the audience in an easy-to-understand manner. The findings from the data can help decision-makers conclude.

Traditionally, data analysis uses the trial and error method. However, on many occasions when there is a huge and varied amount of data, it becomes difficult to use it. Big data was criticized for similar reasons in the past. With a huge amount of data, it becomes very difficult to introduce predictive models that will work without any errors. Traditional solutions used for data analysis do not yield desired results as they are outdated to handle modern-day queries. Due to all these reasons, machine learning applications present better options that can analyze large volumes of data at any given point of time. It is a modern-day solution that is miles ahead of traditional statistics, computer science and other similar applications. Machine learning can develop quick and efficient algorithms that can be used to produce accurate results and analysis of the data.

Data science and machine learning are intertwined and can exist coherently. With the ability to generalize knowledge from data, machine learning needs data and vice-versa. With the increasing use of machine learning, the relevance of data science will only increase in the coming years. The quality of data plays an important role in maintaining the relevance of machine learning. According to experts, machine learning and at least its basic levels will become an important need for data scientists. Evaluating machine learning is one of the most relevant data science skills. Machine learning in data science will immensely help in solving non-standard problems.

Machine learning training will be very beneficial in the coming years for people who belong to the IT sector. With data science and machine learning gaining a lot of prominence and expected to grow rapidly you can enroll yourself for a data science course or machine learning course .

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