Data Science

  Artificial Intelligence , a common concept in the world of technology is precisely Machine Learning and Deep Learning. Machine learning uses algorithms to identify patterns and derive knowledge from the data it uses. Deep learning, on the contrary, is a subset of machine learning that takes Artificial Intelligence closer to the aim of making machines think and function as human beings as possible. When we say that technology influences and shapes tomorrow’s future, we mean that technology will definitely influence the way we live. Progress and loss are the same facets of the coin as artificial intelligence. Technology has some disadvantages and associated problems. Further here, we will discuss some of the challenges in  Artificial Intelligence : A factor of trust and belief: An individual who is not a programmer or engineer can easily explain a deep learning algorithm. Anyone wishing to invest in  Artificial Intelligence to explore new business opportunities  will begin to disappear

  MACHINE LEARNING Machine learning is the study of computer algorithms that helps in analyzing data, learning from the data, and improving from its experience without any human intervention. It is a subset of artificial intelligence that allows the machine to make accurate predictions and automatically learn from the data. TYPES OF MACHINE LEARNING SUPERVISED LEARNING UNSUPERVISED LEARNING REINFORCEMENT LEARNING WHY LEARN MACHINE LEARNING? It helps in increasing the efficiency and helps in understanding the customer better It has brought a new age of personalization for marketing and advertisement campaigns as per the need of the organizations. With the help of Fear Of Missing Out it helps in recommending the products and services to us which other customers have liked. It helps in detecting and catching fraud and suspicious transactions and prevent vicious transactions from happening. It provides better career opportunities with high salary packages in the fields like cybersecurity,

  Deep Learning   is a method of data mining using deep neural network architectures, which in recent years have become increasingly relevant in many typos in artificial intelligence and machine learning algorithms. Deep learning technologies are predicted to affect your life soon. Deep learning helps us to teach machines how to do complex tasks without programming them directly. Let’s learn about the methods to solve a range of problems by deep learning: Fully connected feedforward: Fully connected feedforward neural networks are the traditional network architecture for most simple applications of neural networks. Fully connected means that in the next layer, each neuron is linked to another neuron. And feedforward means that neurons in any previous layer are only connected in a subsequent layer to the neurons. An activation function of each neuron in a neural network affects the output of a neuron given its input. These functions are as follows: Linear Function:  The input is simply