Data Structures, Algorithms, and Machine Learning Optimization Free Download.provides you with a functional, hands-on understanding of the essential computer science for machine learning applications
Learn in Data Structures, Algorithms, and Machine Learning Optimization
- Use “big O” notation to characterize the time efficiency and space efficiency of a given algorithm, enabling you to select or devise the most sensible approach for tackling a particular machine learning problem with the hardware resources available to you.
- Get acquainted with the entire range of the most widely-used Python data structures, including list-, dictionary-, tree-, and graph-based structures.
- Develop a working understanding of all of the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing.
- Discover how the statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you’re solving.
- Understand exactly how the extremely versatile (stochastic) gradient descent optimization algorithm works and how to apply it.
- Familiarize yourself with the “fancy” optimizers that are available for advanced machine learning approaches (e.g., deep learning) and when you should consider using them.
Who Should Take This Course
- You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
- You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
- You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
- You’re a data analyst or AI enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)
- Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information–such as understanding charts and rearranging simple equations–then you should be well-prepared to follow along with all of the mathematics.
- Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
Data Structures, Algorithms, and Machine Learning Optimization Free Download
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