AI And Datascience

Data science is the process of extracting raw and unstructured data combining scientific methods and mathematical formulas, and turning them into structured and filtered data. It uses various tools and techniques to uncover business insights and turn them into actionable solutions. Data scientists, engineers, and executives perform steps like data mining, data cleansing, data aggregation, data manipulation, and data analysis, among others.

The use of data science and artificial intelligence in companies can help them achieve the unthinkable. It can also trigger automation and efficiency in processes that require more workforce and man-hours. Therefore, many industries have incorporated data science and artificial intelligence, which are reaping the benefits that we will be discussing in the next part of the article.

Deep Learing

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

Types of Algorithms used in Deep Learning

1.Convolutional Neural Networks (CNNs)

2.Long Short Term Memory Networks (LSTMs)

3.Recurrent Neural Networks (RNNs)

4.Generative Adversarial Networks (GANs)

5.Radial Basis Function Networks (RBFNs)

6.Multilayer Perceptrons (MLPs)

7.Self Organizing Maps (SOMs)

8.Deep Belief Networks (DBNs)

9.Restricted Boltzmann Machines( RBMs)

10.Autoencoders

Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Machine learning algorithms

Supervised

Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

Unsupervised Learning

unsupervised learning uses unlabeled data. From that data, it discovers patterns that help solve for clustering or association problems. This is particularly useful when subject matter experts are unsure of common properties within a data set.

Semi-supervised Learning

Semi-supervised learning occurs when only part of the given input data has been labeled. Unsupervised and semi-supervised learning can be more appealing alternatives as it can be time-consuming and costly to rely on domain expertise to label data appropriately for supervised learning.

Reinforcement Learning

Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.