Module 1: Linear Algebra for Machine Learning

• Introduction to Linear Algebra: Understand the definition and significance of linear algebra in the realm of machine learning.
• Vectors and Matrices: Dive into operations, properties, and the geometric interpretations of these fundamental constructs.
• Matrix Decompositions: Grasp the concepts of eigenvalues, eigenvectors, and the singular value decomposition and their roles in machine learning.
• Linear Transformations: Explore different types of linear transformations and their applications in data manipulation and interpretation.
• Orthogonality and Least Squares: Discover the world of orthogonal vectors, projection techniques, and their pivotal role in regression analysis.

Module 2: Probability and Statistics for Data Science

• Basics of Probability: Delve into the foundational concepts, including definitions, axioms, conditional probability, and the renowned Bayes’ theorem.
• Random Variables and Distributions: Uncover the intricacies of expectation, variance, and common statistical distributions like the binomial and normal.
• Sampling and Estimations: Understand the Central Limit Theorem, the Law of Large Numbers, and the art of constructing confidence intervals.
• Hypothesis Testing: Learn about p-values, type I and II errors, and various testing methods like the t-test and chi-square test.
• Correlation and Regression: Discern the relationships between variables and dive deep into both simple and multiple regression techniques.

Module 3: Calculus for Machine Learning

• Introduction to Calculus: Recognize the paramount importance of calculus in optimization tasks and in dissecting machine learning algorithms.
• Limits and Continuity: Establish a foundational understanding of the bedrock concepts of calculus.
• Differentiation: Grasp differentiation rules, partial derivatives, and their indispensable application in optimization techniques like gradient descent.
• Integration: Understand the fundamental theorem of calculus, various integration techniques, and their relevance in computing areas under curves.
• Multivariable Calculus: Delve into advanced topics like gradients, Hessians, and their pivotal roles in optimization.

Project Work

To ensure the learning journey comes full circle, students will be given the opportunity to engage in project work using Python during the course. This hands-on approach will solidify the theoretical concepts covered and showcase their practical applications.

After the Course