ARTIFICIAL INTELLIGENCE WORKSHOP🚀

DATA SCIENCE | MACHINE LEARNING

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Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems. Today, the term “AI” describes a wide range of technologies that power many of the services and goods we use every day – from apps that recommend TV shows to chatbots that provide customer support in real time. But do all of these really constitute artificial intelligence as most of us envision it? And if not, then why do we use the term so often? In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further

1.ARTIFICIAL INTELLIGENCE :

*Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP).

*At the simplest level, machine learning uses algorithms trained on data sets to create machine learning models that allow computer systems to perform tasks like making song recommendations, identifying the fastest way to travel to a destination, or translating text from one language to another. Some of the most common examples of AI in use today include

2.DATA SCIENCE :

*Data science is the study of data that helps us derive useful insight for business decision making. Data Science is all about using tools, techniques, and creativity to uncover insights hidden within data. It combines math, computer science, and domain expertise to tackle real-world challenges in a variety of fields.

*Data Science is most promising and high in-demand career path. Given the massive amount of data rapidly increasing in every industry, demand of data scientists is expected to grow further by 35% in 2025. Today’s data science is not limited to only analyzing data, or understanding past trends. Empowered with AI, ML and other advanced techniques, data science can solve real-word problems and train advance systems without human intervention.

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TOOL'S USED FOR DATA SCIENCE

1.PYTHON

2.PANDAS

3.NUMPY

4.GITHUB CO PIOLT

5.SCIKIT-LEARN

3.MACHINE LEARNING

*Machine learning is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. If you're new to this field, this tutorial will provide a comprehensive understanding of machine learning, its types, algorithms, tools, and practical applications.

*Machine learning is fundamentally built upon data, which serves as the foundation for training and testing models. Data consists of inputs (features) and outputs (labels). A model learns patterns during training and is tested on unseen data to evaluate its performance and generalization. In order to make predictions, there are essential steps through which data passes in order to produce a machine learning model that can make predictions.

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TOOL'S USED FOR MACHINE LEARNING

1.IBM WATSON

2.SCIKIT-LEARN

3.PYTORCH

4.AWS

5.GOOGLE COLAB