Course Preview | Professional Certificate in ML and AI from UC Berkeley Executive Education
3:4 min
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Welcome to UC Berkeley 's professional Certificate and Machine Learning and Artificial Intelligence. To get started, we'd like to run through what you can expect from this course. The content for this certificate is broken down into 3 distinct sections.
First, we'll work through some foundational material, including probability, statistics and programming. Then we'll explore various techniques used to model data such as linear regression, logistic regression, decision trees, and neural networks, as well as important supporting concepts like the test train split, the bias variance trade off, and the process of model selection. And finally, we'll spend the last few weeks of the course looking at some advanced topics in machine learning and AI, such as ensemble techniques, recommendation systems, and natural language processing.
Throughout the course we'll have opportunities to put these techniques to practical use in the business setting. About midway through the course you will select your own question to answer using the techniques from our course. And then at the very end of the course you will perform a deep analysis to answer that question. And finally, you will develop a high level presentation for business leaders about how they can apply the answer that you found.
Our goal is that upon completion of this certificate, you will be able to apply real world tools to model and analyze real world data, communicate foundational concepts about machine learning and artificial intelligence, and draw useful conclusions from real world data.
You'll also learn how to identify the best machine learning model to solve a problem such as classification, regression, or time series analysis. And lastly, you will implement the machine learning and data science life cycle. During our time together in this course, you can expect to get your hands dirty with industry standard techniques, but also industry standard tools such as GitHub, Python, Numpy, and Jupiter Notebooks.
As for prerequisites, we presume some prior experience with Python, but have No Fear if your coding skills are a bit rusty. We will provide refresher resources and ample coding demonstrations to aid you throughout the course. Additionally, we expect that you understand basic algebra and an understanding of basic calculus will be helpful, but it's not mandatory.
Now, one final thing to keep in mind. This course includes 24 hour support for technical issues and learning facilitators with experience in machine learning and artificial intelligence. They can both respond to content inquiries and host office hours to further your learning. Do take advantage of these.
Our goal with this course is that you will gain not only a high level understanding of the field of machine learning, but also the ability and the confidence to alley these techniques to challenging problems in your own field. We hope you enjoy the course.
First, we'll work through some foundational material, including probability, statistics and programming. Then we'll explore various techniques used to model data such as linear regression, logistic regression, decision trees, and neural networks, as well as important supporting concepts like the test train split, the bias variance trade off, and the process of model selection. And finally, we'll spend the last few weeks of the course looking at some advanced topics in machine learning and AI, such as ensemble techniques, recommendation systems, and natural language processing.
Throughout the course we'll have opportunities to put these techniques to practical use in the business setting. About midway through the course you will select your own question to answer using the techniques from our course. And then at the very end of the course you will perform a deep analysis to answer that question. And finally, you will develop a high level presentation for business leaders about how they can apply the answer that you found.
Our goal is that upon completion of this certificate, you will be able to apply real world tools to model and analyze real world data, communicate foundational concepts about machine learning and artificial intelligence, and draw useful conclusions from real world data.
You'll also learn how to identify the best machine learning model to solve a problem such as classification, regression, or time series analysis. And lastly, you will implement the machine learning and data science life cycle. During our time together in this course, you can expect to get your hands dirty with industry standard techniques, but also industry standard tools such as GitHub, Python, Numpy, and Jupiter Notebooks.
As for prerequisites, we presume some prior experience with Python, but have No Fear if your coding skills are a bit rusty. We will provide refresher resources and ample coding demonstrations to aid you throughout the course. Additionally, we expect that you understand basic algebra and an understanding of basic calculus will be helpful, but it's not mandatory.
Now, one final thing to keep in mind. This course includes 24 hour support for technical issues and learning facilitators with experience in machine learning and artificial intelligence. They can both respond to content inquiries and host office hours to further your learning. Do take advantage of these.
Our goal with this course is that you will gain not only a high level understanding of the field of machine learning, but also the ability and the confidence to alley these techniques to challenging problems in your own field. We hope you enjoy the course.
