Machine Learning

What is Machine Learning?

Machine learning is a part of artificial intelligence and the subfield of Data Science. It is a growing technology that enables machines to learn from past data and perform a given task automatically. It can be defined as:

"Machine Leaning allows the computers to learn from the past experiences by its own, it uses statistical methods to improve the performance and predict the output without being explicitly programmed."

Difference Between Data Science and Machine Learning

Data Science is the study of data cleansing, preparation, and analysis, while machine learning is a branch of AI and subfield of data science. Data Science and Machine Learning are the two popular modern technologies, and they are growing with an immoderate rate. But these two buzzwords, along with artificial intelligence and deep learning are very confusing term, so it is important to understand how they are different from each other.

Where is Machine Learning used in Data Science?

The use of machine learning in data science can be understood by the development process or life cycle of Data Science. The different steps that occur in Data science lifecycle are as follows:

1. Business Requirements: In this step, we try to understand the requirement for the business problem for which we want to use it. Suppose we want to create a recommendation system, and the business requirement is to increase sales.
2. Data Acquisition: In this step, the data is acquired to solve the given problem. For the recommendation system, we can get the ratings provided by the user for different products, comments, purchase history, etc.
3. Data Processing: In this step, the raw data acquired from the previous step is transformed into a suitable format, so that it can be easily used by the further steps.
4. Data Exploration: It is a step where we understand the patterns of the data, and try to find out the useful insights from the data.
5. Modeling: The data modeling is a step where machine learning algorithms are used. So, this step includes the whole machine learning process. The machine learning process involves importing the data, data cleaning, building a model, training the model, testing the model, and improving the model's efficiency.
6. Deployment & Optimization: This is the last step where the model is deployed on an actual project, and the performance of the model is checked.