CP 322

Machine Learning

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80%

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With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

CP 322

Machine Learning

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.