Math 0-1: Probability for Data Science & Machine Learning

Math 0-1: Probability for Data Science & Machine Learning Coupon Code. A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers

Math 0-1: Probability for Data Science & Machine Learning

Math 0-1: Probability for Data Science & Machine Learning Course. Probability is one of the most important math prerequisites for data science and machine learning. It’s required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call “probability part 2”).

Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).

Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.

What you’ll learn

* Conditional probability, Independence, and Bayes’ Rule

* Use of Venn diagrams and probability trees to visualize probability problems

* Discrete random variables and distributions: Bernoulli, categorical, binomial, geometric, Poisson

* Continuous random variables and distributions: uniform, exponential, normal (Gaussian), Laplace, Gamma, Beta

* Cumulative distribution functions (CDFs), probability mass functions (PMFs), probability density functions (PDFs)

* Joint, marginal, and conditional distributions

* Multivariate distributions, random vectors

* Functions of random variables, sums of random variables, convolution

* Expected values, expectation, mean, and variance

* Skewness, kurtosis, and moments

* Covariance and correlation, covariance matrix, correlation matrix

* Moment generating functions (MGF) and characteristic functions

* Key inequalities like Markov, Chebyshev, Cauchy-Schwartz, Jensen

* Convergence in probability, convergence in distribution, almost sure convergence

* Law of large numbers and the Central Limit Theorem (CLT)

* Applications of probability in machine learning, data science, and reinforcement learning

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Who this course is for:

* Python developers and software developers curious about Data Science

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Instructor

Lazy Programmer Team – The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.

Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master’s degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.

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