Online Course – Certified Professional Internship in Machine Learning and Data Science from Google and DeepLearning.AI

Master the tools of artificial intelligence and machine learning. The Mathematics Methodology for Machine Learning and Data Science are beginner-friendly specializations where you will learn the basic mathematical tools of machine learning: calculus, linear algebra, statistics, and probability theory.

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קורס אונליין - התמחות מקצועית מוסמכת בלמידת מכונה ומדע הנתונים של גוגל ודיפלרנינג.איי

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Bayesian statistics
  • Mathematics
  • Linear regression
  • invoice
  • Machine learning
  • probability

What you will learn in the course

Courses for which the course is suitable

  • Machine Learning Engineer
  • Data Scientist
  • Data Analyst
  • Algorithm developer
  • Artificial Intelligence Expert
  • Systems Analyst
  • Data software developer
  • Machine Learning Researcher

Internship – a three-part course series

Updated to 2024!

Mathematics for Machine Learning and Data Science is an online foundational program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply mathematical concepts through programming. So, in this specialization, you will apply the mathematical concepts you learn through programming in Python, through hands-on lab exercises.

Prerequisites

As a participant in this program, you will need basic to intermediate programming skills in Python to succeed. Many machine learning engineers and data scientists find math difficult, and even experienced practitioners can feel limited by a lack of math skills. This specialization uses innovative math pedagogy to help you learn quickly and intuitively, with courses that use easy-to-follow demonstrations that help you see how the math behind machine learning really works.

Recommendations

  • High school level mathematical knowledge (functions, basic algebra)
  • Introduction to programming (data structures, loops, functions, conditional statements, debugging)

The assignments and labs are written in Python, but the course introduces all the machine learning libraries you will use.

Hands-on Learning Project

Upon completion of this internship, you will be ready to:

  • Represent data as vectors and matrices and identify their properties such as singularity, rank, and linear independence
  • Apply common vector and matrix algebra operations such as dot products, invertibility, and determinants
  • Express matrix operations as linear transformations
  • Apply concepts of eigenvalues ​​and eigenvectors to machine learning problems including principal component analysis (PCA)
  • Optimize various types of functions commonly used in machine learning
  • Perform a shekel reduction in neural programs with different activation functions and cost functions
  • Point out the properties of common probability distributions
  • Perform exploratory data analysis to find, validate, and quantify patterns in a data set
  • Quantify the certainty of predictions made by machine learning models using confidence intervals, margins of error, p-values, and hypothesis testing
  • Apply common statistical methods such as MLE and MAP

Details of the courses that make up the specialization

Linear Algebra for Machine Learning and Data Science

Course 1 • 34 hours • 4.6 (1,674 ratings)

Course Details
What you’ll learn
  • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence.
  • Apply common algebra operations to vectors and matrices such as dot product, inverse, and determinants.
  • Express certain types of matrix operations as linear transformations, and apply concepts of eigenvalues ​​and eigenvectors to machine learning problems.
Skills you will gain
  • Category: Eigenvalues ​​and Eigenvectors
  • Category: Linear equations
  • Category: Determinants
  • Category: Machine Learning
  • Category: Linear Algebra

Calculus for Machine Learning and Data Science

Course 2 • 26 hours • 4.8 (708 ratings)

Course Details
What you’ll learn
  • Perform analytical optimization of various types of functions commonly used in machine learning using properties of derivatives and gradients.
  • Perform approximate optimization of various types of functions commonly used in machine learning.
  • Visually and intuitively understand the derivatives of different types of functions commonly used in machine learning.
  • Perform gradient descent in neural networks with different activation and cost functions.
Skills you will gain
  • Category: Calculation
  • Category: Machine Learning
  • Category: Newton’s method
  • Category: Gradient Descent
  • Category: Mathematical Optimization

Probability & Statistics for Machine Learning & Data Science

Course 3 • 33 hours • 4.6 (447 ratings)

Course Details
What you’ll learn
  • Describe and quantify the uncertainty inherent in predictions made by machine learning models.
  • Visually and intuitively understand the properties of probability distributions commonly used in machine learning and data science.
  • Apply common statistical methods such as maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems.
  • Evaluate the performance of machine learning models using interval estimates and error margins.
Skills you will gain
  • Category: Chance and Statistics
  • Category: Machine Learning (ML) Algorithms
  • Category: Statistical Analysis
  • Category: Chance
  • Category: Statistical hypothesis testing

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