Predictive Analytics using Machine Learning
This course provides an in-depth look at predictive analytics using machine learning. It covers tree-based techniques, linear models, and neural networks, as well as the fundamentals of data pre-processing and model evaluation. Participants have until 15 April 2022 to sign up for the final run of this course. ▼
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Course Feature
Cost:
Free
Provider:
Edx
Certificate:
No Information
Language:
English
Start Date:
15th Mar, 2022
Course Overview
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Updated in [March 06th, 2023]
This course, Predictive Analytics using Machine Learning, is running for the final time and those interested must sign up by 15 April 2022. Participants will gain an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.
The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions. Additionally, participants will learn sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests. Support vector machines will be introduced, including the concept of optimising the separation between classes, before diving into support vector regression. Neural networks will also be covered, including their topology, the concepts of weights, biases, and kernels, and optimisation techniques.
[Applications]
Upon completion of this course, participants can apply the knowledge gained to develop predictive models for a variety of applications. These models can be used to forecast customer behaviour, predict flight delays and cancellations, and classify images and text. Additionally, participants can use sampling techniques such as bagging and boosting, as well as support vector machines and neural networks to improve the robustness and accuracy of their predictive models.
[Career Paths]
1. Data Scientist: Data Scientists use predictive analytics to analyze large datasets and uncover patterns and trends. They use machine learning algorithms to develop models that can predict future outcomes. They also use data visualization techniques to present their findings in a meaningful way. As the demand for data-driven decision-making increases, the demand for Data Scientists is expected to grow.
2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use predictive analytics to build models that can accurately predict outcomes. They also use data engineering techniques to ensure that the data used for training is of high quality. As the demand for machine learning applications increases, the demand for Machine Learning Engineers is expected to grow.
3. Business Intelligence Analyst: Business Intelligence Analysts use predictive analytics to analyze large datasets and uncover patterns and trends. They use machine learning algorithms to develop models that can predict future outcomes. They also use data visualization techniques to present their findings in a meaningful way. As the demand for data-driven decision-making increases, the demand for Business Intelligence Analysts is expected to grow.
4. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based applications. They use predictive analytics to build models that can accurately predict outcomes. They also use data engineering techniques to ensure that the data used for training is of high quality. As the demand for AI-based applications increases, the demand for Artificial Intelligence Engineers is expected to grow.
[Education Paths]
Recommended Degree Paths:
1. Bachelor of Science in Data Science: This degree path provides students with a comprehensive understanding of data science principles and techniques, including machine learning, data mining, and predictive analytics. Students will learn how to use data to solve real-world problems and develop the skills to become a successful data scientist. The degree also covers topics such as data visualization, data engineering, and data management. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision-making.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence (AI) systems and their applications. Students will learn about the fundamentals of AI, including machine learning, natural language processing, and robotics. They will also gain an understanding of the ethical implications of AI and its potential impact on society. This degree is ideal for those who want to pursue a career in AI or related fields.
3. Master of Science in Business Analytics: This degree path provides students with the skills to analyze and interpret data to make informed business decisions. Students will learn about data mining, predictive analytics, and data visualization. They will also gain an understanding of the ethical implications of data-driven decision-making and the potential impact of analytics on business operations. This degree is ideal for those who want to pursue a career in business analytics or related fields.
4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. Students will learn about the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. They will also gain an understanding of the ethical implications of machine learning and its potential impact on society. This degree is ideal for those who want to pursue a career in machine learning or related fields.
Course Provider
Provider Edx's Stats at AZClass
Learners can learn from this course on a variety of topics including predictive modeling, tree-based techniques, support vector machines, and neural networks. They will learn about sampling techniques such as bagging and boosting, and random forests. They will also learn about support vector machines, including optimizing the separation between classes and the concepts of support vector regression. Finally, learners will learn about neural networks, their topology, weights, biases, kernels, and optimization techniques. The course also includes two case studies, Predicting Customer Behavior Following Marketing Campaigns and Predicting Flight Delays and Cancellations, which will help learners apply their knowledge to real-world scenarios.
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