Introduction to Machine Learning
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About this Course:
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptron’s, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with Python, open-source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, Coca Cola, eBay, Snapchat, Uber and many more).
- Understand the formulation of well-specified machine learning problems.
- Learn how to perform supervised and reinforcement learning, with images and temporal sequences.
- Overview: AI and Machine Learning
- Linear Regression
- Gradient Descent
- Genetic Algorithms
- Advanced Genetic Algorithms
Format of this Course:
This course includes lectures, exercises, and labs.
Computer programming (python); Calculus; Linear Algebra
|Subjects||IT Ops & Management|