This course provides an introduction to the fundamentals of Machine Learning. It covers the basic concepts, algorithms, and techniques used in Machine Learning, and provides an overview of the various applications of Machine Learning. The course will cover topics such as supervised and unsupervised learning, linear and non-linear models, decision trees, neural networks, and deep learning. It will also discuss the various evaluation metrics used to measure the performance of Machine Learning models. The course will also provide hands-on experience with popular Machine Learning libraries such as Scikit-Learn, TensorFlow, and Keras. By the end of the course, students will have a good understanding of the fundamentals of Machine Learning and be able to apply them to real-world problems.
What will i learn?
- Understand the fundamentals of Machine Learning algorithms and their applications.
- Develop an understanding of the various techniques used in Machine Learning.
- Students should have a basic understanding of mathematics, statistics, and computer programming.
- Course Materials: Textbook, lecture slides, and other course materials will be provided.
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A case study in predicting house prices
00:40:40
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Regression fundamentals: data & model
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Regression fundamentals
00:09:38
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Regression ML block diagram
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The simple linear regression model
00:13:29
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The cost of using a given line
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Using the fitted line
00:09:22
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Interpreting the fitted line
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Finding maxima or minima analytically
00:06:17
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Finding the max via hill climbing
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