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Solving a Real-World Problem using Machine Learning | ML in Finance Case Study | DSML @SCALER

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What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that enables software applications to grow increasingly effective at predicting outcomes without explicitly programming them to do so. In order to forecast new output values, machine learning algorithms use historical data as input.
What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
What is a business problem?
A business problem is a challenge or issue that impedes the success of a company's operations, goals, or profitability. It can arise from various sources, such as changes in the market, internal processes, or external factors. Identifying and solving business problems is crucial for any organisation's growth and sustainability. Effective problem-solving involves analysing the root cause, developing feasible solutions, and implementing them to mitigate the impact on the business.
What is an ML model?
An ML (Machine Learning) model is a mathematical algorithm or a set of algorithms that can analyse and learn from data to make predictions or decisions without being explicitly programmed. It uses statistical techniques to recognize patterns in data and uses them to make predictions on new data. The model can be trained using supervised, unsupervised, or reinforcement learning techniques depending on the type of problem being solved. An ML model can be applied to various fields, such as image recognition, speech recognition, natural language processing, and predictive analytics.
Topics covered in this video:
00:00 - Introduction
00:50 - Discussing the business problem (Finance domain)
19:23 - Solution for the proposed problem
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