Business Machine Learning faq

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Business Machine Learning is an online course that provides a comprehensive overview of the theoretical foundations of machine learning. Through hands-on coding examples, you will gain the skills to train, optimise, evaluate, and deploy various machine learning models. You will also learn how to select the best models to solve practical problems, fine-tune parameters to improve accuracy, and use hands-on projects and exercises on real-world data sets.

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Course Overview

❗The content presented here is sourced directly from Educative platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [April 29th, 2023]

This course provides an introduction to Business Machine Learning. Students will gain a comprehensive understanding of the theoretical foundations of machine learning, as well as hands-on coding examples to help them understand the concepts. The course will cover the capability of training, optimising, evaluating, and deploying various machine learning models. Students will gain familiarity with the process of selecting the best models to solve practical problems. Hands-on experience with various types of data for machine learning modelling is required. Students will also learn how to fine-tune various parameters in order to improve the accuracy of machine learning models. Finally, students will gain a working understanding of how to use hands-on projects and exercises on real-world data sets.

[Applications]
After completing this course, students should be able to apply the knowledge and skills they have acquired to develop and deploy machine learning models in a business context. They should be able to select the best models to solve practical problems, fine-tune various parameters in order to improve the accuracy of machine learning models, and use hands-on projects and exercises on real-world data sets. Additionally, they should be able to use the theoretical foundations of machine learning to understand the capabilities of training, optimising, evaluating, and deploying various machine learning models.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They must have a strong understanding of the theoretical foundations of machine learning, as well as the ability to code and optimize models. As machine learning becomes more widely used, the demand for Machine Learning Engineers is expected to grow.

2. Data Scientist: Data Scientists are responsible for analyzing and interpreting data to gain insights and make predictions. They must have a strong understanding of data analysis techniques, as well as the ability to use machine learning models to make predictions. As data becomes more widely used, the demand for Data Scientists is expected to grow.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI systems. They must have a strong understanding of the theoretical foundations of AI, as well as the ability to code and optimize AI systems. As AI becomes more widely used, the demand for Artificial Intelligence Engineers is expected to grow.

4. Machine Learning Researcher: Machine Learning Researchers are responsible for researching and developing new machine learning algorithms and models. They must have a strong understanding of the theoretical foundations of machine learning, as well as the ability to code and optimize models. As machine learning becomes more widely used, the demand for Machine Learning Researchers is expected to grow.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and data science. This degree path is becoming increasingly popular as the demand for data-driven solutions grows.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, and robotics. This degree path is becoming increasingly popular as the demand for AI-driven solutions grows.

3. Master of Science in Data Science: This degree path focuses on the development of data-driven solutions. It covers topics such as data mining, machine learning, and predictive analytics. This degree path is becoming increasingly popular as the demand for data-driven solutions grows.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and their applications. It covers topics such as deep learning, reinforcement learning, and natural language processing. This degree path is becoming increasingly popular as the demand for AI-driven solutions grows.

Course Syllabus

Linear Regression

Regularization

Bias-Variance Trade-off

Categorical Features

Logistic Regression

Logistic Regression: Titanic Data

Multiclass Classification and Handling Imbalanced Classes

Project: Predicting Chronic Kidney Disease

K-Nearest Neighbors

Implementation of K-Nearest Neighbors

Logistic Regression vs. KNN

Decision Tree Learning

Bootstrapping and Confidence Interval

Support Vector Machine

Practice and Comparisons

Course Provider

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faq FAQ for Machine Learning Courses

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