Pearson – Probability and Statistics for Machine Learning Sneak Peek

Pearson Probability and Statistics for Machine Learning Sneak Peek-iLLiTERATE
English | Size: 20.78 GB
Category: Tutorial


9 Hours of Video Instruction
Hands-on approach to learning the probability and statistics underlying machine learning

Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and statistical modeling, with a focus on machine learning applications.

About the Instructor

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in person at Columbia University and New York University, as well as online via O’Reilly, YouTube, and the SuperDataScience podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times.

Skill Level
Intermediate

Learn How To
Understand the appropriate variable type and probability distribution for representing a given class of data
Calculate all of the standard summary metrics for describing probability distributions, as well as the standard techniques for assessing the relationships between distributions
Apply information theory to quantify the proportion of valuable signal that’s present among the noise of a given probability distribution
Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t-test, ANOVA, and R-squared
Understand the fundamentals of both frequentist and Bayesian statistics, as well as appreciate when one of these approaches is appropriate for the problem you’re solving
Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep learning to a given problem
Develop a deep understanding of what’s going on beneath the hood of predictive statistical models and machine learning algorithms

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!)

Course Requirements
Mathematics: Familiarity with secondary school-level mathematics will make it easier for you to follow along with the class. 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.

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