Python AI Fundamentals

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
• 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