Data Foundations for Machine Learning

Learn the #1 skill required to succeed as a machine learning engineer or data scientist

Math and Statistics

Unlike other math and statistics courses, this foundations series is built from the ground up to boost your understanding of machine learning principles. 

  • Available on-demand Data Primer Course

    Data is the essential building block of Data Science, Machine Learning, and AI. This course is the first in the series and is designed to teach you the foundational skills and knowledge required to understand, work with, and analyze data.
  • Available on-demand SQL Primer Course

    This SQL coding course teaches students the basics of Structured Query Language, which is a standard programming language used for managing and manipulating data and an essential tool in AI.  The course covers topics such as database design and normalization, data wrangling, aggregate functions, subqueries, and join operations,
  • Available on-demand Programming Primer Course with Python

    The Python language is one of the most popular programming languages in data science and machine learning as it offers a number of powerful and accessible libraries and frameworks specifically designed for these fields. This programming course is designed to give participants a quick introduction to the basics of coding using the Python language.
  • Available on-demand Introduction to AI

    This AI literacy course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models.
  • Available on-demand Data Wrangling with Python

    Data Wrangling with Python Course  Data wrangling is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain. In preparation for […]
  • Available on-demand Introduction to Machine Learning

    In an introductory machine learning live training, key topics include defining machine learning, distinguishing supervised and unsupervised learning, covering basic concepts, exploring common algorithms (e.g., decision trees, neural networks)
  • Available on-demand Introduction to Large Langue Models and Prompt Engineering

    In the rapidly evolving field of AI, the “LLMs, Prompt Engineering, and Generative AI” course stands as a cutting-edge offering, designed to equip learners with the latest advancements in Large Language Models (LLMs), prompt engineering, and generative AI techniques.
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How It Works

  • The foundations series is available on demand.

  • Each course is available on-demand as soon as you register.

  • Study the courses in order or skip the subjects you are already know.

  • Each course includes exercises to improve learning outcomes.

  • Coding demos allow you to learn hands-on skills.

  • Learn at your own pace. Courses can be taken alongside additional Ai+ courses.

Interactive Sessions

Hands-On Coding Demos

Learning Comprehension Exercises

What You Will Learn

Not only will you learn the core mathematical concepts, but you will also learn how they are applied to machine learning. In addition, you will learn to apply your knowledge using some of the key machine learning and deep learning platforms, such as Tensorflow and PyTorch.

Linear Algebra

Data Structures for Algebra

  • What Linear Algebra Is, A Brief History of Algebra

  • Vectors and Vector Transposition

  • Norms and Unit Vectors

  • Basis, Orthogonal, and Orthonormal Vectors

  • Arrays in NumPy, Matrices

  • Tensors in TensorFlow and PyTorch

Common Tensor Operations

  • Tensors, Scalars

  • Tensor Transposition

  • Basic Tensor Arithmetic

  • Reduction

  • The Dot Product

  • Solving Linear Systems

Matrix Properties

  • The Frobenius Norm

  • Matrix Multiplication

  • Symmetric and Identity Matrices

  • Matrix Inversion

  • Diagonal Matrices

  • Orthogonal Matrices


  • Eigenvectors

  • Eigenvalues

  • Matrix Determinants

  • Matrix Decomposition

  • Application of Eigendecomposition

Matrix Operations for Machine Learning

  • Singular Value Decomposition (SVD)

  • The Moore-Penrose Pseudoinverse

  • The Trace Operator

  • Principal Component Analysis (PCA): A Simple Machine Learning Algorithm

  • Resources for Further Study of Linear Algebra



  • What Calculus is

  • A Brief History of Calculus

  • The Method of Exhaustion

  • Matrix Decomposition

  • Application of Eigendecomposition

Computing Derivatives with Differentiation

  • The Delta Method

  • Basic Derivative Properties

  • The Power Rule

  • The Sum Rule

  • The Product Rule

  • The Quotient Rule & The Chain Rule

Automatic Differentiation

  • AutoDiff with Pytorch

  • AutoDiff with TensorFlow 2

  • Relating Differentiation to Machine Learning

  • Cost (or Loss) Functions

  • The Future: Differentiable Programming

Gradients Applied to Machine Learning

  • Partial Derivatives of Multivariate Functions

  • The Partial-Derivative Chain Rule

  • Cost (or Loss) Functions

  • Gradients

  • Gradient Descent

  • Backpropagation

  • Higher-Order Partial Derivatives


  • Binary Classification

  • The Confusion Matrix

  • The Receiver-Operating Characteristic (ROC) Curve

  • Calculating Integrals Manually

  • Numeric Integration with Python

  • Finding the Area Under the ROC Curve

  • Resources for Further Study of Calculus

Probability and Statistics

Introduction to Probability

  • What Probability Theory Is

  • Applications of Probability to Machine Learning

  • Discrete vs Continuous Variables

  • Probability Density Function 

  • Expected Value

  • Measures of Central Tendency

  • Quantiles: Quartiles, Deciles, and Percentiles

  • Measures of Dispersion:

  • Covariance and Correlation

  • Marginal and Conditional Probabilities

Distribution in Machine Learning

  • Uniforms

  • Gaussian: Normal and Standard Normal

  • The Central Limit Theorem

  • Log-Normal

  • Binominal and Multinomial

  • Poisson

  • Mixture Distributions

  • Preprocessing Data for Model Input

 Information Theory

  • What Information Theory Is

  • Self-Information

  • Nats, Bits and Shannons

  • Shannon and Differential Entropy

  • Kullback-Leibler Divergence

  • Cross-Entropy

Frequentist Statistics

  • Frequentist vs Bayesian Statistics

  • Review of Relevant Probability Theory

  • Z-scores and Outliers

  • P-values

  • Comparing Means with t-tests

  • Confidence Intervals

  • ANOVA: Analysis of Variance

  • Pearson Correlation Coefficient

  • R-Squared Coefficient 

  • Correlation vs Causation

  • Multiple Comparisons


  • Features: Independent vs Dependent Variables

  • Linear Regression to Predict Continuous Values

  • Fitting a Line to Points on a Cartesian Plane

  • Ordinary Least Squares

  • Logistic Regression to Predict Categories

  • (Deep) ML vs Frequentist Statistics

 Bayesian Statistics

  • When to Use Bayesian Statistics

  • Prior Probabilities

  • Bayes’ Theorem

  • PyMC3 Notebook

  • Resources for Further Study of Probability and Statistics

Computer Science

Introduction to Data Structures and Algorithms

  • Introduction to Data Structures

  • Introduction to Computer Algorithms

  • A Brief History of Data

  • A Brief History of Algorithms

  • “Big O” Notation for Time and Space Complexity

Lists and Dictionaries

  • List-Based Data Structures: Arrays, Linked Lists, Stacks, Queues, and Deques

  • Searching and Sorting: Binary, Bubble, Merge, and Quick

  • Set-Based Data Structures: Maps and Dictionaries

  • Tables, Load Factors, and Maps

Trees and Graphs

  • Trees: Decision Trees, Random Forests, and Gradient-Boosting (XGBoost)

  • Graphs: Terminology, Directed Acyclic Graphs (DAGs)

  • Resources for Further Study of Data Structures & Algorithms

The Machine Learning Approach to Optimization & Fancy Deep Learning Optimizers

  • The Statistical Approach to Regression: Ordinary Least Squares

  • When Statistical Approaches to Optimization Break Down

  • The Machine Learning Solution

  • A Layer of Artificial Neurons in PyTorch

  • Jacobian Matrices

  • Hessian Matrices and Second-Order Optimization

  • Momentum

  • Nesterov Momentum

  • AdaGrad, AdaDelta, RMSProp, Adam, Nadam

  • Training a Deep Neural Net

  • Resources for Further Study

Gradient Descent

  • Objective Functions

  • Cost / Loss / Error Functions

  • Minimizing Cost with Gradient Descent

  • Learning Rate

  • Critical Points, incl. Saddle Points

  • Gradient Descent from Scratch with PyTorch

  • The Global Minimum and Local Minima

  • Mini-Batches and Stochastic Gradient Descent (SGD)

  • Learning Rate Scheduling

  • Maximizing Reward with Gradient Ascent

What is Data Literacy? 

Data Literacy build the vocabulary and insights that allow you to “speak data”. It is the ability to ask and answer meaningful questions by collecting, analysing, and making sense of data. 

Being data literate means you can:

Data literate means you can:

Discover and take advantage of trends.

Understand how predictive models work .

Discover hidden patterns in data

Identify opportunities for new products and services.

Data Literacy is at the heart of data science and machine learning.

It will provide a foundation to help you grasp and learn the  math, programming, engineering, visualization, and modeling in machine learning.

Data for All

Anyone can tell a story with data, not just data scientists. Many professional roles can benefit including marketing, analysts, engineers, and journalists.

This  beautiful crafted but simple multi-line chart add impact and insight to a story. 

Image credit:

Open Data Science

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