Overview
Learn Math essentials for Data science,Data analysis,Machine Learning and Artificial intelligence
Syllabus
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- Introduction to Mathematics for Data Science
-- Overview of Data Science, Data Analysis, and Machine Learning
-- Importance of Mathematics in Technological Fields
- Linear Algebra
-- Vectors and Matrices
-- Matrix Operations
-- Eigenvalues and Eigenvectors
-- Applications in Data Science
- Statistics and Probability
-- Descriptive Statistics: Mean, Median, Mode
-- Inferential Statistics: Hypothesis Testing, Confidence Intervals
-- Probability Fundamentals
-- Probability Distributions: Normal, Binomial, Poisson
-- Bayesian Statistics and Applications
- Calculus
-- Limits and Continuity
-- Derivatives and their Applications
-- Integrals and their Applications
-- Multivariable Calculus and Partial Derivatives
-- Optimization in Machine Learning
- Geometry
-- Coordinate Systems
-- Geometric Transformations
-- Distance Metrics and their Role in Machine Learning
- Advanced Topics
-- Principal Component Analysis (PCA) for Dimensionality Reduction
-- Singular Value Decomposition (SVD)
-- Introduction to Mathematical Optimization Techniques
- Python for Math and Data Science
-- Basic Python for Data Analysis
-- Using Libraries: Numpy, Pandas, Matplotlib
-- Implementing Mathematical Concepts using Python
- Conclusion
-- Synthesis of Mathematical Tools in Data Science
-- Future Directions and Advanced Learning Paths
- Practical Applications and Projects
-- Projects exploring real-world applications
-- Data set analysis using mathematical techniques
Taught by
Tags
On-Demand
