WA3418

# Applied Linear Algebra, Calculus, and Statistics for AI, ML, and Data Science Training

Understanding the core concepts of artificial intelligence, machine learning, or data science is impossible without knowing the fundamentals of linear algebra, calculus, statistics, and probability. This training course teaches the essentials in the respective fields of knowledge to prepare the learners to start or advance their careers in AI, machine learning, or data science.

Course Details

2 days

### Target Audience

• Software developers
• IT architects
• Technical and product managers
• Designers
• Data analysts
• Data engineers

### Skills Gained

• Gain a deep understanding of AI, ML, and Data Science fundamentals to accelerate further development
• Solve systems of linear equations using Gaussian elimination
• Perform vector operations, such as addition, subtraction, and dot product
• Apply derivatives to optimize squared loss and log loss
• Understand probability distributions and statistical inference
Course Outline
• Applied Linear Algebra for Artificial Intelligence, Machine Learning, and Data Science
• Systems of Linear Equations
• Singular vs non-singular matrices
• Linear dependence, independence, and the determinant
• Matrix row-reduction (Gaussian elimination)
• Rank of a matrix and row echelon form
• Systems of Linear Equations in AI, Machine Learning, and Data Science
• Vector Operations and Linear Transformations
• Vectors and their properties
• Vector operations
• Linear transformations
• Matrix multiplication
• Determinants and Eigenvectors
• Machine Learning and matrices
• Applied Calculus for Artificial Intelligence, Machine Learning, and Data Science
• Derivatives and optimization for AI, Machine Learning, and Data Science
• Common derivatives and derivative properties
Optimization of squared loss and log loss
• Partial derivatives, gradients, and optimization
• Derivatives, optimization, and gradient descent in AI, Machine Learning, and Data Science
• Applied Probability & Statistics for Artificial Intelligence, Machine Learning, and Data Science
• Probability
• Probability, Conditional Probability, Bayes Theorem, and Independence
• Bayes Theorem, Naive Assumption, and The Naive Bayes Model
• Probability Distributions
• Discrete and continuous distributions
• Normal, Binomial, Bernoulli, Uniform, and Chi-Squared distributions
• Probability Density Function and Probability Mass Function
• Cumulative Probability, Cumulative Distribution, and Cumulative Distribution Function
• Multivariate Probability Distributions and Covariance
• Probability Distributions in AI, Machine Learning, and Data Science
• Statistical Sampling, Estimation, and Inference
• Population and sample
• Point Estimation
• Maximum Likelihood Estimation
• Linear regression
• Regularization
• Maximum a Posteriori Estimation
• Central Limit Theorem
• Statistical Inference: Confidence Intervals and Hypothesis Testing
• A/B Testing
• Statistical Sampling, Estimation, and Inference in AI, Machine Learning, and Data Science