Machine Learning vs. Artificial Intelligence

Artificial Intelligence

Artificial intelligence (AI) is a field of computer science focused on making computers more intelligent so that they can imitate intelligent human behavior.

AI is a medium to enable computers to learn and engage in human-like thought processes, such as learning, reasoning and self-correction.

Machine Learning

The introduction of machine learning enabled computers to learn real-world knowledge by identifying patterns from the data, self-correction from this learning process to enable decision making.

Machine learning algorithms also suffered from a trade-off as their performance was dependent on how data was presented to them.

Deep Learning

Deep learning algorithms solved this problem by extracting information by itself.

Deep learning is a particular type of machine learning that makes the computer learning more powerful, flexible and abstract.

Data science is a broader term that includes the process for building this machine learning model, including data collection, data processing, data analysis, data visualization, modeling, making predictions, and so forth.

What is the big data?

It is the amount of data that traditional database software tools cannot manage and analyze due to the complexity and size.

Data points are created in our daily activities, such as:

Social media activity (Twitter tweets and messages; Facebook likes, shares and messages) – Search engine activity (Google searches, creating web pages).
Video streaming (watching or uploading videos on YouTube or Netflix).
Payments (using credit cards, internet banking).

Data Science and Big Data in Industry Practice

Data science project lifecycle:

Before starting to work on a data science task, the first step is to define the objective of the business problem clearly.

This involves answering questions about different aspects of the problem, such as:

1
What is the business problem and how to translate it into a data science problem?
2
What data is required for the problem and how to collect and to prepare it?
3
What insights can be generated from the data and what machine learning/deep learning models can be useful for the problem?
4
How to build and evaluate the model, define success (KPIs, metrics, and so on) for the solution of the problem?
5
How to measure the impact of a machine learning model in the real world?