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The Tech Academy — Artificial Intelligence Developer Course

The Tech Academy's AI Developer Course

Description

Artificial intelligence (computer systems capable of performing tasks that typically require human intelligence) is arguably the fastest-growing technology sector on Earth. The ramifications of AI exist in all industries and it is pervading every corner of our lives. This course covers each fundamental skill and tool that an entry-level AI developer needs to know.

Topics covered

  • AI essentials and AI development tools
  • Machine learning (computers acquiring knowledge from data to improve performance over time)
  • Neural networks (computational frameworks inspired by the human brain, used for pattern recognition and decision-making)
  • AI development with Python
  • Data analysis (the process of examining, interpreting, and drawing insights from data to inform decision-making)
  • R programming (a statistical and data analysis language widely used for data manipulation and visualization)
  • Data science fundamentals
  • Big data (large and complex sets of data that require specialized techniques for processing and analysis)
  • Data structures
  • Natural language processing (the study of enabling computers to understand and process human language)
  • Deep learning (advanced machine learning using neural networks to learn patterns and make accurate predictions)
  • Data visualization (presenting data in visual formats to aid understanding and uncover insights effectively)
  • Chatbots (AI-powered computer programs that simulate human conversation to provide interactive assistance, such as ChatGPT)
  • Web scraping (extracting data from websites automatically) and data mining (the process of discovering meaningful patterns from large sets of data)
  • Fundamentals of mathematics
  • Statistics and probability
  • Basic AI algorithms
  • OpenAI API (a popular platform that offers access to advanced language models for various applications and tasks)
  • Data preprocessing (the initial step in data analysis, involving cleaning, transforming, and organizing data)
  • Feature engineering (the process of creating informative and relevant input variables for machine learning models)
  • Model training and model evaluation (training a model on data and assessing its performance)
  • Sentiment analysis (the process of determining the sentiment or emotion expressed in textual data)
  • Containerization with Docker
  • Popular data science and AI-centric libraries like: Pandas, Anaconda, TensorFlow, Keras, Theano, SciPy, PyTorch and Matplotlib.

and more…