AI ML Keywords –

Machine Learning:

• Machine learning, a subset of artificial intelligence, allows systems to discover patterns in data and then get easier with experience.

Artificial Intelligence:

• A machine’s ability to replicate as humans do, such as perceiving, learning, reasoning, and solving problems.

Dataset:

• A data set is a group of data, these data can be in numerical data, categorical data, correlated data.

Supervised:

• The use of labelled datasets defines the machine learning strategy known as supervised learning. Labeled inputs and outputs allow the model to analyze its precision and improve over time.

Unsupervised:

• Unsupervised learning analyses and groups unlabeled data sets using machine learning methods. For anomaly detection, recommendation engines, customer personas, and medical imaging, unsupervised learning is a wonderful fit.

Linear Regression:

• A variable’s value can be predicted using linear regression analysis depending on the value of another variable. Supervised machine learning techniques include linear regression.

Logistic Regression:

• A statistical analysis technique called logistic regression uses the data from a data set to predict a binary outcome, such as yes or no. One illustration of supervised learning is logistic regression.

Data Collection:

• Data collection is the process of gathering data from various offline and online sources by scraping, collecting, and loading it.

Data Preparation:

• Data preparation is also known as data “pre-processing. For the purposes of machine learning projects, data preparation is the process of obtaining, combining, cleaning, and processing raw data.

Evaluate:

• Accuracy, precision, and recall are the three primary measures used to evaluate the efficiency of a classification algorithm.

Predictions:

• When predicting the likelihood of a specific outcome, “prediction” refers to the result of an algorithm that has been trained on past data and applied to current data.

Pattern:

• A pattern is a series of data that takes on a recognized form, which analysts then look for in the present data.

Natural Language Processing:

• natural language processing is a form of Ai technology (AI), enables machines to recognize and interpret human language in addition to reading it.

Analysis:

• Before beginning to use machine learning, one must first perform data analysis, which involves obtaining, cleaning, exploring, and visualizing data.

Robot:

• A robot is a type of automated machine that can carry out particular activities quickly and accurately.

Cleaning:

• the process of getting data ready for analysis by getting rid of or changing data that is missing, inaccurate, redundant, or formatted incorrectly.

Train:

• Simply said, training a model involves learning (trying to decide) appropriate values for each weight and bias from labelled samples.

Test:

• the procedure by which a fully trained model’s performance is evaluated on a test dataset.