Course for Conscripts (1991)

Become a specialist in machine learning and mathematical analysis to decipher complex data and create innovative AI solutions

WHAT IS INCLUDED?

Math

Math

Course details

7 months

5-7 days a week, 2-3 hours each lesson

79.000 AMD / month

Social payment refunds are available for this program

Decode the Data. Build the Intelligence.

Timeline

Introductory interview

We get to know you, you get to know us. We discuss your goals and expectations.

Informational meeting

By attending the information session, you’ll learn all the details about the program and have the opportunity to ask your questions.

The road to success

Projects → Exams → Certificate → Work → Career →

Application

One step - and your journey begins.

Slack Onboarding

You sign up for the Slack platform, join your group, and get to know the professors.

First lesson

Your true transformation begins here.

Application

One step - and your journey begins.

Introductory interview

We get to know you, you get to know us. We discuss your goals and expectations.

Slack Onboarding

You sign up for the Slack platform, join your group, and get to know the professors.

Informational meeting

By attending the information session, you’ll learn all the details about the program and have the opportunity to ask your questions.

First lesson

Your true transformation begins here.

The road to success

Projects → Exams → Certificate → Work → Career →

Course Structure

Python basics

Intro to CS, C vs Python
Collections and syntax
Functions and Git
Numerical sequences, limits, and systems of linear equations
Consolidation

Python advanced

Iterations and files
Modules and error handling
Object-oriented programming
Taylor series, basis and dimensionality of linear spaces, and the Cauchy-Schwarz inequality
Data structures and algorithms

Data analysis with Python

NumPy
Pandas
Visualization and SQL
Multivariable functions, partial derivatives, and matrix decompositions (LU and QR)
Exploratory data analysis (EDA)

Supervised learning

Foundations
Regression
Classification and evaluation
Definite integrals, conditional extrema, correlation, covariance, and Singular Value Decomposition (SVD)
Probabilistic and instance-based

Trees, ensembles and unsupervised learning

Support vector machines
Trees and ensembles
Gradient boosting
Double integrals, random variables, mathematical expectation, and probability distributions
Unsupervised learning

Neural networks and CNNs

Foundations
Training neural networks
Making training work
Improper integrals, characteristic functions, and KL-Divergence
CNNs in depth

Sequence models, transformers and generative models

Sequence models
Transformers
NLP, LLMs and generative models
Fourier series, central limit theorem, and entropy
Capstone and final

Instructors

John Asatryan

Head of Education Department

Picsart Academy

Hayk Darbinyan

Teacher of Computer Architecture

Having a strong base of knowledge gives you the opportunity to create something great—not just copy others, avoid problems, or complain about them.

Picsart Academy

Hayarpi Ghazaryan

Senior Product Deisgner

Product design is never finished, only refined.

SOFTSWISS

Tatev Kishoyan

Digital Marketing & Demand Generation Specialist

Marketing is psychology in motion. It all starts in the brain.

ZAPTEST AI

Levon Mezhlumyan

C++ Lecturer

Picsart Academy

Vardan Grigoryan

Software Engineer

Become a Dragon Slayer

Picsart Academy

Armen Amirjanyan

GameDev Lecturer

Picsart Academy

Course Structure

Python basics

Intro to CS, C vs Python
Collections and syntax
Functions and Git
Numerical sequences, limits, and systems of linear equations
Consolidation

Python advanced

Iterations and files
Modules and error handling
Object-oriented programming
Taylor series, basis and dimensionality of linear spaces, and the Cauchy-Schwarz inequality
Data structures and algorithms

Data analysis with Python

NumPy
Pandas
Visualization and SQL
Multivariable functions, partial derivatives, and matrix decompositions (LU and QR)
Exploratory data analysis (EDA)

Supervised learning

Foundations
Regression
Classification and evaluation
Definite integrals, conditional extrema, correlation, covariance, and Singular Value Decomposition (SVD)
Probabilistic and instance-based

Trees, ensembles and unsupervised learning

Support vector machines
Trees and ensembles
Gradient boosting
Double integrals, random variables, mathematical expectation, and probability distributions
Unsupervised learning

Neural networks and CNNs

Foundations
Training neural networks
Making training work
Improper integrals, characteristic functions, and KL-Divergence
CNNs in depth

Sequence models, transformers and generative models

Sequence models
Transformers
NLP, LLMs and generative models
Fourier series, central limit theorem, and entropy
Capstone and final