This chapter will take you from novice to confident coder in a matter of weeks, and make sure that everyone is comfortable with the material required for the rest of the course. You’ll learn concepts fundamental to programming and solidify your understanding through the challenges assigned throughout the course.
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Module 1 - Set up and understanding
   Git bash
    Python 3
    VSCode
    Jupyter Notebook
    How does a computer process information?
    “Hello World” program
    What is syntax?
    Flow control
    Basic Python built-in functions
    Expressions, Values and Variables

Module 2 - Logic and control flow
    Logical and comparison operators
    Conditional statements
    While loops, For loops and iterables

Module 3 - String manipulation and object methods
    Basic string manipulation
    Immutability of strings
    String operations

Module 4 - Numerical methods and complexity
    Guess and check
    Bisection search
    Computational Complexity and Big O notation
    Time and space efficiency of programs
    Classes of program complexity

Module 5 - Functions
    Decomposition/abstraction
    Function definitions and calls
    Scope
    Return statement
    Functions as objects

Module 6 - More data types
    Compound data types
    Tuples, lists and dictionaries

Module 7 - Sets and recursion
    Set theory and notation
    Set objects and operations
    Recursion

Module 8 - Object oriented programming and error handling
    What are classes, methods and attributes?
    Inheritance
   Magic functions
    Exceptions and error handling

Module 9 - Computational Modelling
    Best, average and worst case modelling

As data plays a larger part in every organisation, especially digital ones, it becomes more and more necessary to understand where data comes from, how it's stored, and how it's manipulated.
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Module 1 - Introduction.
    Introduction to data pipelines
    Introduction to APIs
    HTTP requests
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Module 2 - Web scraping.
    Selenium for web scraping
    Performing basic web automation actions
    Referencing HTML elements using Xpath

Module 3 - SQL.
    SQL basics
    SQL Join operations
    SQL Aggregations
    SQL Subqueries
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Module 4 - File structures and Pandas
    Understand standard file structures like CSV, XML, etc
    Using Pandas library to load in and manipulate data files
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Module 5 - Data cleaning
    Data cleaning techniques and how to implement them
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Module 6 - Descriptive statistics
    What is descriptive statistics and how to use it
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Module 7 - How to deal with missing data
    Data imputation, removal and other data cleaning techniques
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Module 8 - Data visualization
    Visualising data in Python using Plotly

This chapter is a comprehensive introduction to the foundations of machine learning (ML). There is a deep focus on being practical - actually building every algorithm you learn and knowing how and when to apply it.

We’ll begin with an introduction to the basic machine learning problems and the different types of ML. In each successive session, you'll learn the theory behind a particular algorithm before implementing it and using it on a real dataset.
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Module 1 - Introduction to machine learning
    Scikit-learn
    Validation and testing
    Hyperparameters, grid search and K-fold cross validation
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Module 2 - Gradient methods
    Linear regression
    Automatic differentiation
    Classification
    Multiclass classification
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Module 3 - Theory
    Evaluation metrics
    Fitting Polynomials and the curse of dimensionality
    Bias & variance, underfitting and overfittting
    Regularisation
    Maximum Likelihood Estimation (MLE)
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Module 4 - Supervised learning techniques
    K-nearest neighbours
    Regression trees
    Classification trees
    Support Vector Machines (SVMs)

Module 5 - Unsupervised learning techniques
    K-means clustering
    DBSCAN
    PCA and t-SNE

Data science is one of the fastest growing fields as measured by the number of people employed in it. It's core goal is to be able to pick out insights that can help optimize business processes.
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Module 1 - Introduction to data science
    Evaluation Metrics
    Hypothesis testing
    Statistical modelling
    Multicollinearity
    Influential points
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Module 2 - Case Study
    House prices regression case study
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Module 3 - Time series modelling
    ARMA and ARIMA
    Holt Winters forecasting
    Facebook’s forecasting tool, Prophet
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Module 4 - A/B testing
    How to run and evaluate A/B tests

Module 5 - Recommendation systems
    Content based recommendation systems
    Collaborative filtering
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Module 6 - Traditional NLP
    Topic modelling for document summarization
    Sentiment Analysis

Deep Learning has become continuously more important and impressive in the past few years. Through the use of Artificial Neural Networks, the field of AI has been propelled into the spotlight. Computers can now do things thought impossible just years ago and the boundaries are being pushed rapidly by new research. 
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This chapter will build upon your knowledge of traditional Machine Learning and comprehensively cover Deep Learning. You will learn about the different Neural Network architectures, what they are used for, why they work well on particular types of data and how to implement them using the PyTorch library.
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You will be equipped with the foundational knowledge and skills to be able to read the latest papers and implement them. As well as use pre-trained models from the web and tweak them to your own needs.

Module 1 - Intro to Pytorch
   Intro to PyTorch Deep Learning library
    What is Autograd?
    Computational Graphs
    Implementing Linear Regression in PyTorch
    PyTorch Datasets and Dataloaders
    Applying augmentation using PyTorch transforms
    Saving and Loading models
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Module 2 - Tensorboard
    Introduction to Tensorboard
    How to write data to Tensorboard
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Module 3 - Neural networks
    The maths behind Neural Networks
    Implementing Neural Networks in PyTorch
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Module 4 - Optimisation for deep learning
    The maths behind different optimisers (SGD, AdaGrad, RMSProp, Adam)
    Using optimisers with PyTorch
    Hyperparameter tuning using the Ray library
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Module 5 - Custom datasets
    Building custom datasets in PyTorch
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Module 6 - Convolutional Neural Networks
    The maths behind Convolutional Neural Networks (CNNs)
    Implementing CNNs in PyTorch
    ResNets
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Module 7 - Regularisation for deep learning
    Dropout
    Batch Normalization
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Module 8 - GPU acceleration and cloud training
    What is a GPU and what kind of operations can GPUs accelerate?
    Using a GPU to train PyTorch models
    Using Amazon Web Service’s (AWS's) EC2 service to train models in the cloud
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Module 9 - Using pre-trained models
    Using pre-trained models from PyTorch
    Transfer learning
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Module 10 - Object detection and image segmentation
    Single instance object detection problem
    Training a CNN for segmentation task
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Module 11 - Autoencoders
    Intro to autoencoders
    Variational Autoencoders
    Convolutional Autoencoders
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Module 12 - Recurrent Neural Networks (RNNs)
    RNNs
    Long Short Term Memory Networks (LSTM)
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Module 13 - Generative Adversarial Networks (GANs)
    GANs
    Deep Convolutional GANs (DCGAN)
    Generating fake images of fashion and faces using GANs

NLP is the most implemented in enterprise among sub-fields of AI. In the digital age, language is still key to communication and being able to analyse the large swathes of data collected within an organization will propel their efficiency forward.
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Through the use of neural networks, specifically the transformer architecture, results thought impossible have been produced. There is a headline about some baseline record being broken every few months. You don't have to search for long to find large passages of writing online written by AI that are indistinguishable from human-written content.

Due to the largely open nature of AI research, there are pre-trained models that cost hundreds of thousands of dollars to train which are available for public access. There is a wave of new startups that are built on top of these releases.

Module 1 - Word embeddings
    Creating representations of words with dense embeddings
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Module 2 - Recurrent Neural Networks for NLP
    Language modelling
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Module 3 - Sequence to sequence tasks and attention
    Implementing seq-to-seq for machine translation
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Module 4 - Self-attention
    Self attention
    Transformers
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Module 5 - Custom NLP datasets
    Creating custom NLP datasets using TorchText
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Module 6 - Using the state of the art
    Bidirectional Encoding Representations using Transformers (BERT)
    Using the HuggingFace Python library

Having learnt about how to build and train different Machine Learning models in Python, you now need to be able to deploy these models so they can be used on the web and other applications by real users.
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This chapter will guide you through the different options available for deploying your model, whether they are basic regression functions or large neural networks. 

Module 1 - Building an API
    Using the Flask Python library to create an API
    Using Docker to run your code on any machine
    Automating code running using Cron
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Module 2 - Cloud services
    Deploying a Docker container on AWS EC2
    AWS AI services
    Google Cloud Platform AI services
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Module 3 - Monitoring and orchestration
    Apache Airflow
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Module 4 - Basic web development and ONNX
    HTML, CSS and JS basics
    Making API requests from a web application
    Using ONNX to make your model transportable
    Running neural networks in the browser using ONNX