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Data Science Essential Math for Data Science E-Learning

Product description
Essential Math for Data Science E-Learning Mathematics form the foundation for Machine Learning algorithms and Data Science, necessary for working and research in the Data Science field. Many Data Science elements depend on mathematical concepts such as probability, statistics, calculus, linear algebra, and so on. Hence, it is important for data s...

Essential Math for Data Science E-Learning

Mathematics form the foundation for Machine Learning algorithms and Data Science, necessary for working and research in the Data Science field. Many Data Science elements depend on mathematical concepts such as probability, statistics, calculus, linear algebra, and so on. Hence, it is important for data scientists, to under-stand the principles of these concepts and how these principles might affect their models and day-to-day tasks.

In this Essential Math for Data Science Learning Kit, you will explore important concepts of mathematics that form the foundation for Machine Learning algorithms, Data Science and Artificial Intelligence..

Learning Kits are structured learning paths, mainly within the Emerging Tech area. A Learning Kit keeps
the student working toward an overall goal, helping them to achieve your career aspirations. Each part takes the student step by step through a diverse set of topic areas. Learning Kits are
made up of required tracks, which contain all of the learning resources available such as Assessments (Final Exams), Mentor, Practice Labs and of course E learning. And all resources with a 365 days access from first activation.

Course content

Track 1: Introduction to Math

In this track, you will focus on the fundamentals of linear algebra and calculus. This includes discrete math concepts and their implementations, theoretical and practical guide to calculus, exploring linear algebra, and matrix operations.
Courses (12 hours +):
              Math & Optimizations: Introducing Sets & Set Operations
              Math & Optimizations: Introducing Graphs & Graph Operations
              Math & Optimizations: Solving Optimization Problems Using Linear Programming
              Math & Optimizations: Solving Optimization Problems Using Integer Programming
              Calculus: Getting Started with Derivatives
              Calculus: Derivatives with Linear and Quadratic Functions & Partial Derivatives
              Calculus: Understanding Integration
              Essential Maths: Exploring Linear Algebra
              Matrix Decomposition: Getting Started with Matrix Decomposition
              Matrix Decomposition: Using Eigendecomposition & Singular Value Decomposition

Track 2: Statistics and Probability

In this track, you will acquire a deeper understanding of probability and statistical concepts including probability distributions, various types of statistical tests, and hypothesis testing. You will deep dive into understanding conditional probability concepts that forms the crux of naïve Bayes classification algorithms.
Courses (17 hours +)
              Core Statistical Concepts: An Overview of Statistics & Sampling
              Core Statistical Concepts: Statistics & Sampling with Python
              Probability Theory: Getting Started with Probability
              Probability Theory: Understanding Joint, Marginal, & Conditional Probability
              Probability Theory: Creating Bayesian Models
              Probability Distributions: Getting Started with Probability Distributions
              Probability Distributions: Uniform, Binomial, & Poisson Distributions
              Probability Distributions: Understanding Normal Distributions
              Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing
              Statistical & Hypothesis Tests: Using the One-sample T-test
              Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests
              Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis

Track 3: Math Behind ML Algorithms

In this track, the focus will be on math applied in various machine learning algorithms. You will understand the intuition behind these algorithms along with math used in their optimization/loss/cost functions. You will understand the math behind regression algorithms, decision trees, distance-based models, kernel methods and SVM and neural networks.
Courses (12 hours +)
              Regression Math: Getting Started with Linear Regression
              Regression Math: Using Gradient Descent & Logistic Regression
              The Math Behind Decision Trees: An Exploration of Decision Trees
              Distance-based Models: Overview of Distance-based Metrics & Algorithms
              Distance-based Models: Implementing Distance-based Algorithms
              Support Vector Machine (SVM) Math: A Conceptual Look at Support Vector Machines
              Support Vector Machine (SVM) Math: Building & Applying SVM Models in Python
              Neural Network Mathematics: Understanding the Mathematics of a Neuron
              Neural Network Mathematics: Exploring the Math behind Gradient Descent

Track 4: Advanced Math

In this module, the focus will be on statistical analysis and modeling in R. Explore probability distributions, statistical tests, regression analysis, clustering, and regularized models.
Courses (2 hours +)
              ML & Dimensionality Reduction: Performing Principal Component Analysis
              Recommender Systems: Under the Hood of Recommendation Systems

Product specifications

Availabilty
45 hours
Language
English
Certificate of participation
Yes
Online access
365 days

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€189,00 Excl. tax
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