
Applied Machine Learning: Foundations Online Class





This online class provides the foundational skills needed to apply machine learning to solve real-world problems. Students will gain the knowledge and tools to generate impactful insights with the power of machine learning.▼
Course Feature
Cost:
Free Trial
Provider:
LinkedIn Learning
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
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Updated in [March 06th, 2023]
Applied Machine Learning: Foundations Online Class provides learners with the foundational knowledge and skills needed to build and optimize machine learning models. Learners will gain an understanding of the workflow used to build effective machine learning models, the methods used to optimize those models, and the techniques used to evaluate the performance of those models. Additionally, learners will gain an understanding of the various types of machine learning algorithms and how to apply them to solve real-world problems. Finally, learners will gain an understanding of the various tools and technologies used to develop and deploy machine learning models.
By taking this course, learners will gain the skills and knowledge needed to build and optimize machine learning models, as well as the ability to evaluate the performance of those models. Learners will also gain an understanding of the various types of machine learning algorithms and how to apply them to solve real-world problems. Additionally, learners will gain an understanding of the various tools and technologies used to develop and deploy machine learning models. Finally, learners will gain an understanding of the workflow used to build effective machine learning models and the methods used to optimize those models.
[Applications]
The Applied Machine Learning: Foundations Online Class provides students with the foundational knowledge and skills necessary to apply machine learning to real-world problems. Students will learn the fundamentals of machine learning, including supervised and unsupervised learning, data pre-processing, feature engineering, model selection, and model optimization.
After completing this course, students should be able to apply machine learning to their own projects. They should be able to identify the appropriate machine learning algorithms for a given problem, pre-process data, engineer features, select and optimize models, and interpret the results. Additionally, students should be able to identify and address potential issues with their models, such as overfitting and underfitting.
[Career Paths]
1. Data Scientist: Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use a variety of techniques, such as machine learning, to develop predictive models and uncover insights. Data Scientists are in high demand as organizations increasingly rely on data-driven decision making.
2. Machine Learning Engineer: Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use a variety of techniques, such as deep learning, to develop models that can be used to solve complex problems. Machine Learning Engineers are in high demand as organizations increasingly rely on machine learning to automate processes and improve decision making.
3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for designing, developing, and deploying AI-based systems. They use a variety of techniques, such as natural language processing, to develop AI-based systems that can be used to automate processes and improve decision making. Artificial Intelligence Engineers are in high demand as organizations increasingly rely on AI to automate processes and improve decision making.
4. Business Intelligence Analyst: Business Intelligence Analysts are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use a variety of techniques, such as machine learning, to develop predictive models and uncover insights. Business Intelligence Analysts are in high demand as organizations increasingly rely on data-driven decision making.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree program provides students with a comprehensive understanding of computer science fundamentals, including programming, data structures, algorithms, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. This degree is ideal for those interested in developing and applying machine learning models to solve real-world problems.
2. Master of Science in Artificial Intelligence: This degree program focuses on the development of intelligent systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and data mining. This degree is ideal for those interested in developing and applying advanced machine learning models to solve complex problems.
3. Doctor of Philosophy in Machine Learning: This degree program focuses on the development of advanced machine learning algorithms and their applications. It covers topics such as deep learning, reinforcement learning, natural language processing, computer vision, and robotics. This degree is ideal for those interested in developing and applying cutting-edge machine learning models to solve complex problems.
4. Master of Science in Data Science: This degree program focuses on the development of data-driven solutions and their applications. It covers topics such as data mining, machine learning, natural language processing, computer vision, and robotics. This degree is ideal for those interested in developing and applying data-driven models to solve real-world problems.
The demand for professionals with expertise in machine learning and artificial intelligence is growing rapidly, and these degree paths provide students with the necessary skills and knowledge to succeed in this field. Additionally, the development of new technologies such as deep learning and reinforcement learning is creating new opportunities for those with the right skills and knowledge.
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