Zero-to-AI-Engineer-Roadmap-and-Resources

A comprehensive roadmap and curated resources for aspiring AI Engineers, covering foundational knowledge, core AI concepts, practical experience, projects and how to prepare for these roles.

Table of Contents

🧰 Foundational Knowledge

• Linear Algebra
• Calculus
• Probability and Statistics
• Optimization Theory
• Information Theory

💻 Computer Science Fundamentals

• Programming Languages
  • Python
  • R
• Data Structures and Algorithms
• Object Oriented Programming Concepts(OOPS)
• SQL and DBMS
• Computer Networks
• Operating Systems
• Computer Architecture

🔥 Data Concepts

• Data Analysis, Manipulation and Cleaning

  • Data Cleaning Techniques (Handling Missing Values, Outlier Detection)
    • Automated EDA Tools
      • SweetViz
      • ydata-profiling
      • DataPrep
      • AutoViz
      • D-Tale
      • dabl
      • QuickDA
      • Lux
  • Data Preprocessing (Normalization, Standardization)
  • Feature Engineering

• Data Visualization

  • Data Visualization Libraries (Matplotlib, Seaborn, ggplot2)
  • Dashboarding Tools (Tableau, Power BI)
  • Open-Source Tools: Apache Superset, Redash

• Data Engineering

  • Fundamentals of Data Engineering
  • ETL (Extract, Transform, Load) Processes
  • ELT (Extract, Load, Transform) Processes
  • Data Warehousing Concepts
  • Batch Processing
    • Spark/PySpark
  • Data Pipelines and Workflow Management
    • Apache Airflow
  • Deploying Data Pipelines in Production
  • Real-Time Streaming
    • Apache Kafka
  • Cloud Computing
    • AWS
    • GCP
    • Azure
  • DataOps
    • Docker
    • Kubernetes
  • Modern Data Stack
    • Dbt
    • Airbyte
    • Fivetran
  • 📚 Recommended Books
    • Designing Data-Intensive Applications
    • Fundamentals of Data Engineering
    • The Data Warehouse Toolkit

• Big Data Technologies

  • Fundamentals of Big Data
  • Databases: Relational Databases: MySQL, PostgreSQL, NoSQL: MongoDB, Cassandra
  • Hadoop Ecosystem (HDFS, MapReduce, Hive, Pig)
  • Apache Spark (RDDs, Spark SQL, MLlib)
  • Distributed Computing Concepts
  • Data Warehouses (Amazon Redshift, Google BigQuery, Snowflake, Azure Synapse)

• Time Series Analysis

🟢 Core AI Concepts

• Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  • Swarn Intelligence (ASI)
• Deep Learning
  • Neural Networks
    • Convolutional Neural Networks (CNNs)
      • ANNs
      • Recurrent Neural Networks (RNNs)

Practical Projects

• 300+ Practical Data Science Projects to Practice

Advanced Topics and Specialization

• Natural Language Processing (NLP)
• Transformers
• Advanced NLP Techniques
• Computer Vision/Image Processing
• Object Detection and Segmentation
• Reinforcement Learning (Advanced)
• Deep Q-Networks (DQNs)
  • Applications