AI Academy

Linear Algebra for Machine Learning and Data Science

Instructor: Luis Serrano Duration: 7 hours to complete
Objective 1 Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Objective 2 Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Objective 3 Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems
Image Analysis
Dimensionality Reduction
Linear Algebra
Data Science
NumPy
Artificial Intelligence
Jupyter
Applied Mathematics
Machine Learning Methods
Python Programming
Data Manipulation

Calculus for Machine Learning and Data Science

Instructor: Luis Serrano Duration: 1 week at 10 hours a week
Objective 1 Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
Objective 2 Approximately optimize different types of functions commonly used in machine learning
Objective 3 Visually interpret differentiation of different types of functions commonly used in machine learning
Objective 4 Perform gradient descent in neural networks with different activation and cost functions
Machine Learning
Python Programming
Numerical Analysis
Artificial Neural Networks
Regression Analysis
Mathematical Modeling
Applied Mathematics
Deep Learning
Calculus
Derivatives

Probability & Statistics for Machine Learning & Data Science

Instructor: Luis Serrano Duration: 4 hours to complete
Objective 1 Describe and quantify the uncertainty inherent in predictions made by machine learning models
Objective 2 Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science
Objective 3 Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems
Objective 4 Assess the performance of machine learning models using interval estimates and margin of errors
Data Science
Bayesian Statistics
Probability Distribution
Statistical Hypothesis Testing
Statistical Visualization
Probability & Statistics
A/B Testing
Probability
Statistical Analysis
Statistical Machine Learning
Descriptive Statistics
Statistical Inference
Sampling (Statistics)
Exploratory Data Analysis