AI Driven Mathematical and Statistics Foundations for Machine Learning
Essentials of Mathematics and Statistics to get started with Data Science and Machine Learning. A rigorous and engaging deep-dive into statistics and machine learning. Unlock the power of machine learning foundations. Learn Python basics for data manipulation and analysis. Master fundamental statistical concepts and techniques. Dive into matrix algebra and hypothesis testing. Apply regression analysis for predictive modeling.
Offer ends in:
What you'll get
- 8+ Hours
- 1 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Essential Python skills for data manipulation and analysis.
- Fundamental statistical concepts including sampling, probability, and distributions.
- Matrix algebra operations for machine learning applications.
- Hypothesis testing techniques and regression analysis for predictive modeling.
- Descriptive statistics (mean, variance, etc)
- Inferential statistics
- T-tests, correlation, ANOVA, regression, clustering
- The math behind the "black box" statistical methods
- How to implement statistical methods in code
- How to interpret statistics correctly and avoid common misunderstandings
- Coding techniques in Python and MATLAB/Octave
- Machine learning methods like clustering, predictive analysis, classification, and data cleaning
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Machine Learning & AI with Python | Mathematics & Statistics 8h 21m ✔
Description
This comprehensive course provides a foundational understanding of mathematical and statistical concepts essential for machine learning, with a focus on practical implementation using Python. Beginning with an introduction to machine learning, participants are introduced to Python programming for data manipulation, visualization, and analysis. The curriculum progresses to cover fundamental statistical concepts such as sampling, data types, probability, random variables, and distributions. Participants also learn matrix algebra for machine learning applications and delve into hypothesis testing techniques and regression analysis.
Introduction to Machine Learning with Python:
Participants are introduced to the field of machine learning and its applications. They learn how to use Python for machine learning tasks, including data preprocessing, model training, and evaluation.
Importing:
Participants learn how to import Python libraries and modules necessary for machine learning tasks, including NumPy, Pandas, Matplotlib, and Scikit-learn.
Basics of Statistics Sampling:
This section covers fundamental concepts of statistics sampling, including random sampling, stratified sampling, and sampling distributions.
Basics of Statistics Data Types and Visualization:
Participants learn about different types of data and techniques for data visualization using Python libraries such as Matplotlib and Seaborn.
Basics of Statistics Probability:
Participants gain an understanding of probability theory, including probability distributions, conditional probability, and Bayes' theorem.
Basics of Statistics Random Variables:
This section covers random variables, probability mass functions, probability density functions, and cumulative distribution functions.
Basics of Statistics Distributions:
Participants learn about common probability distributions such as the normal distribution, binomial distribution, and Poisson distribution.
Matrix Algebra:
Participants learn about matrix algebra concepts and operations, including matrix multiplication, matrix inversion, and eigenvalues.
Hypothesis Testing:
Participants learn about hypothesis testing techniques, including null and alternative hypotheses, p-values, and significance levels.
Hypothesis Tests-Types:
This section covers different types of hypothesis tests, including t-tests, chi-square tests, and ANOVA tests.
Regression:
Participants learn about regression analysis, including linear regression, polynomial regression, and logistic regression.
Throughout the course, participants engage in practical exercises and projects to reinforce their learning and apply mathematical and statistical concepts to real-world machine learning problems using Python. By the end of the course, participants will have the knowledge and skills to leverage Python for data analysis, visualization, and statistical modeling in machine learning applications.
Requirements
- Good work ethic and motivation to learn.
- Previous background in statistics or machine learning is not necessary.
Target Audience
- Students taking statistics or machine learning courses
- Professionals who need to learn statistics and machine learning
- Scientists who want to understand their data analyses
- Anyone who wants to see "under the hood" of machine learning
- Artificial intelligence (AI) students
- Business intelligence students
Course Ratings
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I really enjoyed this course, and found the statistical approaches useful - especially the regression and cluster sampling sections. The course pacing was easy to understand, and the topics covered in a comprehensive but not over-explained way. Thanks for giving such an interesting and useful course!
Matthew Rolley