Python AI Fundamentals

500.00 +Iva

Duração: 1 dia
Próxima Data: 20/04/2022
Área: Python
Certificação Associada: N/A
Local: Lisboa e Porto

*Curso disponível em Live Training

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This one day workshop will introduce to the terminology, tools and high level considerations that need to be considered and understood to ensure the best possible outcome for an AI implementation.



This course is designed for anyone with Python programming experience wanting to gain a solid foundation in Python’s data analysis libraries. It is a must for aspiring Data Analysts and Scientists. Existing Data Analysts wanting a systematic introduction to Python’s Data Analysis tools would also find the course very useful.


Module 1: AI Tools

Module 2: Data Science Languages

Module 3: The Role of Python in Artificial Intelligence

Module 4: Python Libraries for Artificial Intelligence

  • NumPy: NumPy the computing library for Python.
  • SciPy: SciPy is an advanced library containing algorithms that are used for data science
  • scikit-learn: scikit-learn is Python’s main machine learning library
  • NLTK: Library for natural language processing
  • TensorFlow: TensorFlow is Google’s neural network library used for implementing deep learning artificial intelligence

Module 5: Understanding the Role of Algorithms

  • Planning and branching
  • Local search and heuristics

Module 6: Using expert systems

Module 7: Hardware

  • Standard Hardware
  • Von Neumann bottleneck
  • Single points of failure
  • Tasking and multitasking

Module 8: Specialised Hardware

  • Graphic Processing Units (GPUs)
  • Why are GPU’s suited to this field?
  • Application Specific Integrated Circuits (ASICs):
  • Field Programmable Gate Arrays (FPGAs):
  • Specialized Sensors

Module 9: Data Powers AI

  • What is Data Science?
  • Big Data
  • Data Structures and Formats
  • Data Sources
  • Data Storage

Module 10: Data Quality and Readiness

  • Data quality and readiness is key to a successful implementation. intelligence is based on knowledge and data is the raw material
  • Balance
  • Representative
  • Completeness
  • Clean Data

Module 11: Predictive Analytics

  • Regression
  • Classification

Module 12: Data Analysis for AI

  • Transforming: Changes the data’s appearance
  • Cleansing: Fixes imperfect data.
  • Inspecting: Validates the data.
  • Modelling: Discovers the relationship between the elements present in data.

Module 13: Define Machine Learning

Module 14: How machine learning works

Module 15: What are the benefits of machine learning?

  • Automation:
  • Fraud detection:
  • Customer service:
  • Resource scheduling:
  • Resource scheduling:
  • Safety systems:
  • Machine efficiency

Module 16: Learning Models

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Module 17: Machine learning approaches

  • Naïve Bayes:
  • Bayesian networks graph:
  • Decision trees

Module 18: Enhancing AI with Deep Learning

  • Simple neural networks
  • The strength of the connection between neurons in the network
  • Continuous learning using online learning
  • Reusable solutions using transfer learning
  • End-to-end learning


A desire to understand where AI can be beneficial to your business.

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