In Artificial Intelligence, there are agents which perceive the environment via sensors and act upon the environment through actuators or effectors. Just like humans have sensors through which we sense our surroundings (eyes, ears, nose, tongue, and skin) and actuators (limbs) to perform actions on these surroundings. The agent starts from the initial state and performs a series of actions in order to reach the goal state. For instance, a vacuum cleaner agent will perform actions of moving right and left, and sucking in dirt to reach the goal of successfully cleaning the environment.

Planning and its types

This activity of coming up with a sequence of actions in order to accomplish the target or goal is called as Planning. Planning can be Classical or Non-classical. In case of Classical Planning, the environment is fully observable, deterministic, static and discrete, whereas in case of Non-classical Planning, the environment is partially observable (i.e. the entire state of the environment is not visible at a given instant) or non- deterministic (or stochastic, i.e. the current state and chosen action cannot completely determine the next state of the environment). …

Data Augmentation is a technique that aims to expand existing data by making slight modifications to the data. In NLP, it is often used to increase size of the training data and improve performance of the model. Here we will look at Data Augmentation using:

• Word Embeddings
• BERT
• Back Translation
• T5

Data Augmentation using Word Embeddings

Let’s first look at augmentation of data using Word Embeddings. Using word embeddings, we can represent words as vectors in a high-dimensional space in a meaningful way. For instance, we have two sentences:

Winter is Coming.

Any man who must say “I am the king” is no true king.

To expand our data, we can replace keywords, i.e. words that are more relevant and give meaning to the sentence. …

Artificial Intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence possessed by humans. It is used to describe machines that mimic cognitive functions that humans associate with the human mind such as learning and problem-solving. Today, it is transforming the way we interact, consume information, and obtain goods and services across industries. In health care, AI is bringing paradigm changes. It is already changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates. Recently AI techniques have sent vast waves across health care, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future! They will definitely help physicians make better clinical decisions or even replace human judgment in certain functional areas of health care. The increasing availability of health care data and rapid development of big data analytic methods have made possible the recent successful applications of AI in health care. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making. Recently the term “big data” too has gained immense popularity all over the world. Data from various sources such as customer data from hospitals, pharmacies, patient history, cellphones, and internet searches serve as a vast pool of data that can be utilized for clinical purposes. Scientists have been working on improving public health strategies, medical research, and the care provided to patients by analyzing these big datasets. …

Homo sapiens are set apart from other species by their capacity for language. There are two main reasons why we need computer agents to be able to process natural languages:

• To communicate with humans
• To acquire information from written language

Natural Language Processing is a branch of Artificial Intelligence used to deal with human languages. It helps machines understand, interpret and manipulate our language. A large amount of unstructured and semi-structured data is generated each day from a myriad of sources. NLP is used to handle this huge volume of speech and text data and gain valuable insights from it. A lot of the data generated could be in different dialects or could include slang often used on social media. …

Before understanding MBA, let’s see what are frequent patterns.

Frequent patterns are itemsets, subsequences, or substructures which appear frequently in a data set. For instance, a set of items, such as milk and bread, that are frequently bought together. A subsequence, such as first buying some milk, then eggs, and then coffee, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If it occurs frequently, it is called a (frequent) structured pattern. Finding these patterns is of utmost importance in mining associations, correlations, and many other interesting relationships. …

Every business revolves around customers and interactions carried out with customers. It is said that a customer is the boss of a business and every interaction with him counts! An infallible way of dealing with this pressing subject is the use of Chatbots. Chatbots are used to conduct an online chat conversation via text or text-to-speech and provide direct contact with a live human agent. With rising demands all over the world, they have gained immense popularity and are widely used in a myriad of industries to render a pleasant and uniform customer experience. They can be used to answer FAQs, handle customer queries and grievances, manage bookings, make recommendations, CRM and provide 24*7 customer support. …

The famous saying “There is no strength without unity” perfectly captures the idea behind Ensemble Learning. Ensemble techniques are widely used in statistics and machine learning to improve the performance of a model. They use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. There are mainly two types of ensemble techniques: Bagging and Boosting.

In Bagging, individual models are trained on random subsets of the original dataset and consequently, their individual predictions are aggregated. Hence Bagging is also called as Bootstrap Aggregation.

Let’s take an example.

Consider an original dataset D and various base models such as M1, M2, M3 and so on. For every model, we provide a sample of the dataset. The training set for each of the base classifiers is independent of each other. Some of the original data may be repeated in the resulting training set while the rest may be left out. …

The three pillars of Data Science are:

• Linear Algebra
• Statistics
• Optimization
• What is optimization?

“An optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from an allowed set and computing the value of the function. ”

It is useful in finding the best solution to a problem (which could be minimizing or maximizing the functional form f(x)). Here x stands for decision variables. We choose values for x so that this function f is either maximized or minimized. There might be certain constraints on x which have to be satisfied while solving the optimization problem i.e. …