Data Science, Artificial Intelligence, and Machine Learning, these three terms have created a lot of buzz in recent times. Data Science has been popularly coined as “Enticing Job of @1st Century”.This blog-post will cover everything you need to know about these terms being used synonymously:
Nowadays every company is using AI and ML to make their product more attractive. Before diving into AI, ML, Data Science one must know what exactly is data?
What is Data?
Now, if we mention data mainly within the sector of science, then the answer to “what is data” goes to be that data is differing kinds of knowledge that sometimes is formatted in a particular manner. All the software is split into two major categories, and people are programs and data. Programs are the gathering made up of instructions that are used to manipulate data. So, now after understanding what is data and data science, allow us to learn some fantastic facts.
How To Analyze Data?
There are two ways to research the data:
- Data Analysis in Qualitative Research
- Data Analysis in Quantitative Research
1. Data Analysis in Qualitative Research
Data analysis and research in subjective information work somewhat better than numerical information because the quality information consists of words, portrayals, pictures, objects, and sometimes images. Getting knowledge from such entangled data may be a confounded procedure thus, it’s usually utilized for exploratory research also as data analysis.
2. Data Analysis in Quantitative Research
Preparing Data for Analysis
The primary stage in research and analysis of knowledge is to try to it for the examination with the goal that the nominal information is often changed over into something important. The preparation of knowledge comprises the subsequent.
- Data Validation
- Data Editing
- Data Coding
For quantitative statistical research, the use of descriptive analysis regularly gives supreme numbers. However, the analysis isn’t capable to show the justification behind those numbers. Still, it’s important to believe the simplest technique to be utilized for research and analysis of knowledge fitting your review survey and what story specialists got to tell.
What is Artificial Intelligence?
Artificial intelligence (AI) makes it possible for machines to seek out from experience, suit new inputs, and perform human-like tasks. Most AI examples that you simply hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and tongue processing. Using these technologies, computers are often trained to accomplish specific tasks by processing large amounts of data and recognizing patterns within the info.
AI History
- In 1956, American computer scientist John McCarthy organized the Dartmouth Conference, at which the term ‘Artificial Intelligence’ was first adopted.
- In 1951, a machine known as Ferranti Mark 1 successfully used an algorithm to master checkers.
- John McCarthy, often known as the father of AI, developed the LISP programming language which became important in machine learning.
- In the late 1960s, computer scientists worked on Machine Vision Learning and developing machine learning in robots. WABOT-1, the first ‘intelligent’ humanoid robot, was built in Japan in 1972.
Types of AI
- Weak AI embodies a system designed to carry out one particular job. Weak AI systems include video games just like the chess example from above and personal assistants like Amazon’s Alexa and Apple’s Siri. You ask the assistant a problem , it answers it for you.
- Strong AI systems are systems that keep it up to the tasks considered to be human-like. These tend to be more complex and complicated systems. they’re programmed to handle situations during which they’ll be required to problem solve without having a private intervene. These kinds of systems are often found in applications like self-driving cars or in hospital operating rooms.
What is Machine Learning?
Machine Learning (ML) has proven to be one of the foremost game-changing technological advancements of the past decade. within the increasingly competitive corporate world, ML is enabling companies to fast-track digital transformation and enter an age of automation. Some might even argue that AI/ML is required to remain relevant in some verticals, like digital payments and fraud detection in banking or product recommendations.
The eventual adoption of machine learning algorithms and their pervasiveness in enterprises is additionally well-documented, with different companies adopting machine learning at scale across verticals.
Types of Machine Learning
1. Supervised Learning: Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the info must be labeled accurately for this method to figure, supervised learning is extremely powerful when utilized in the proper circumstances. In supervised learning, the ML algorithm is given a little training dataset to figure with. This training dataset may be a smaller part of the larger dataset and serves to offer the algorithm a basic idea of the matter, solution, and data points to be addressed. The training dataset is additionally very almost like the ultimate dataset in its characteristics and provides the algorithm with the labeled parameters required for the matter.
Example: Suppose you’re given a basket crammed with different sorts of fruits. Now the primary step is to coach the machine with all different fruits one by one like this:
- If shape of object is rounded and depression at top having color Red then it will be labeled as –Apple.
- If shape of object is long curving cylinder having color Green-Yellow then it will be labeled as –Banana.
2. Unsupervised Learning: Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor isn’t required to form the dataset machine-readable, allowing much larger datasets to be worked on by the program. In supervised learning, the labels allow the algorithm to seek out the precise nature of the connection between any two data points. However, unsupervised learning doesn’t have labels to figure off of, leading to the creation of hidden structures. Relationships between data points are perceived by the algorithm in an abstract manner, with no input required from the citizenry.
Example: Suppose there is an image of dogs and cats which have not seen ever.
Thus the machine has no idea about the features of dogs and cats so we can’t categorize it in dogs and cats. It can categorize them according to their similarities, patterns, and differences so that we can easily categorize the above picture into two parts:
- the first part contains all the pictures of dogs in it
- the second part contains all the pictures of cats in it.
3. Reinforcement Learning: Reinforcement learning directly takes inspiration from how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’. In typical reinforcement learning use-cases, such as finding the shortest route between two points on a map, the solution is not an absolute value. Instead, it takes on a score of effectiveness, expressed in a percentage value. The higher this percentage value is, the more reward is given to the algorithm. Thus, the program is trained to give the best possible solution for the best possible reward.
Example:
In this case,
- Your cat is an agent that is exposed to the environment. An example of a state could be your cat sitting, and you use a specific word for cat to walk.
- Our agent reacts by performing an action transition from one “state” to another “state.”
- For example, your cat goes from sitting to walking.
- The reaction of an agent is an action, and the policy is a method of selecting an action given a state in expectation of better outcomes.
- After the transition, they may get a reward or penalty in return.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) may be a branch of AI that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computing and linguistics, in its pursuit to fill the gap between human communication and computer understanding.
Why is NLP Important?
- Large volumes of textual data
Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. - Structuring a highly unstructured data source
Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there many languages and dialects, but within each language may be a unique set of grammar and syntax rules, terms and slang. When we write, we frequently misspell or abbreviate words, or omit punctuation. When we speak, we’ve regional accents, and that we mumble, stutter and borrow terms from other languages.
Conclusion:
By now, you would have had a thorough understanding of what AI, ML, NLP is and how this concept has evolved over a period of time. Artificial Intelligence will be the driving force for innovations in the upcoming time and have the potential to solve the world’s striking problems from various domains.
Related References
- Decision Tree Algorithm Introduction
- Natural Language Processing with Python
- An Introduction to Reinforcement Learning
- Data Science And Machine Learning: Hands-On Labs With Python
Leave a Reply