Infinity Labs R&D

Artificial Intelligence Syllabus

Infinity’s Curriculum Philosophy

Our goal is to provide all participants the skills and know-how to be ever-evolving Artificial Intelligence experts. Even as they learn one particular technical stack, Infinity provides participants with what it takes to be well-rounded, “can-do” professionals who can independently migrate to new platforms and technologies throughout their careers. Our training equips them with the fundamentals and resources that enable this flexible technical agility.

Our goal and distinguishing hallmark is the transformation of our trainees into consummate professionals, who are not only capable of independently learning new technologies, but whose work habits and professionalism reflect the highest level of excellence. Our proprietary syllabi are designed to enable our participants to reach those objectives.

Unlock the future with AI: Elevate your skills, ignite innovation, and chart new horizons through our transformative AI learning journey.

The most complete training program for the most needed skill of the 21th century.

Infinity Labs R&D created a groundbreaking complete training program to transform University graduates to the future leaders of the AI revolution.

Become a complete professional, by acquiring knowledge and skills that will allow you to understand the full data / AI ecosystem and be a key part of it. Develop strong technical and analytical skills, adopt a scientific approach and push it to the limit with a creative mindset to create the most advanced Big Data and Machine Learning architectures. Scale ideas, optimize computational power, leverage the best models, get the most value from data.

Data is the oil of the 21st century. In this training participants will master all the steps, from exploration to consumption through extracting and refining.

1st stage: Software Development

Our AI training track begins with Software Development syllabus that encompasses the core fundamentals and pre-requisites that every ML and software professional needs to know.

Goals and high-level skill set:

  • Python programming language
  • PEP 8 coding standards
  • Abstract Data Types
  • Algorithms and Data structures
  • Linux, Shell & Bash
  • Structured programming
  • Object Oriented programming
  • Functional programming
  • API development
  • Interface design
  • Complexity theory and practice
  • Unit testing, regression testing, smoke testing
  • Debugging techniques
  • CI/CD principles
  • Package release
  • Dependency Handling
  • Deployment and containerization
  • Industry-quality deliverables

2nd Stage: Big Data

After completion of the Software Development stage, participants continue to the Big Data stage where they master the art of collecting, extracting, preparing, loading data, using state of the art tools and libraries.

Goals and high-level skill set:

  • Data Acquisition
  • SQL and no-SQL databases
  • Hadoop, HDFS and MapReduce
  • Spark and PySpark
  • ELK stack
  • ETL Processes
  • Data Pipelines
  • Data Streams
  • Data Warehouse
  • Data Scraping
  • File Formats
  • REST / RESTful API
  • Asynchronous communication
  • Cloud Computing
  • Process Automation

3rd stage: Machine Learning

After completion of the Big Data stage, participants continue to the Machine Learning stage. In this last stage they complete their skills set with Machine Learning and Deep Learning theory and real-life applications.

Goals and high-level skill set:

  • Statistics
  • Data Processing
  • Data Analysis
  • Exploratory Data Analysis
  • Feature Selection
  • Generative AI
  • Statistical Modelling
  • Supervised and Unsupervised Learning
  • Regression Models
  • Classification Models
  • Model Evaluation and Selection
  • Ensemble Models
  • Clustering Methods
  • Dimensionality Reduction
  • Anomaly Detection
  • Data Pipelines and ML Pipelines
  • Solution Deployment
  • Reinforcement Learning
  • Neural Networks
  • Optimization techniques
  • Multilayer Perceptron and backpropagation
  • Contrastive Learning
  • Computer Vision
  • Convolutional Neural Networks (CNN)
  • Natural Language Processing (NLP)
  • Recurrent Neural Networks
  • Integrating pre-trained models
  • Best practices, methodology and workflow