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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, visualised, shared and manipulated.
Module 1 - Data collection
Introduction to data pipelines
Introduction to APIs
Web scraping using requests and Selenium
Module 2 - Data formats & the Pandas Python library
Important file formats
Intro to Pandas
Module 3 - Data cleaning & exploratory data analysis (EDA)
Reasons for and approaches to data cleaning
Handling missing data
Module 4 - SQL
SQL Join operations
Creating databases and importing data
Module 5 - Extract, Transform, Load (ETL)
ETL using Pandas, SQLAlchemy
Module 6 - Intro to cloud
Intro to Amazon Web Services (AWS)
The AWS CLI & Boto3
This unit covers a comprehensive overview of data science. We'll start by learning how to visualise and explore data using industry standard tools. Modelling data is a key part of data science, and a key tool for doing this is machine learning (ML).
Module 1 - Data Science Basics
Module 2 - Introduction to machine learning
Intro to ML & when (NOT) to use
Validation and testing
Hyperparameters, grid search and K-fold cross validation
Feature selection and feature engineering
Module 3 - Theory
Bias & variance, underfitting and overfittting
Maximum Likelihood Estimation (MLE)
Module 4 - More supervised models
Module 5 - Unsupervised models
Dimensionality reduction using principal component analysis (PCA) & T-SNE
In this unit, we introduce deep learning models, a class of machine learning models that are able to represent much more complex relationships in data. This means they can reach state of the art results on tasks that involve processing images, audio, video and other complex data.
Module 1 - PyTorch
PyTorch Datasets and DataLoaders
Making custom datasets
Module 2 - Neural networks
Optimisation for deep learning
Convolutional Neural Networks (CNNs)
Module 3 - Practical tips
Architecture tips, data augmentation & debugging tips
Hardware acceleration (GPUs & TPUs)
Module 4 - Advanced Architectures
Module 5 - Applications
Content based recommendation systems
In the ML Engineering part of this unit, we'll focus on the engineering required to put models into the real world. Then in the MLOps part, we'll focus on the tools which are becoming industry standards for ensuring that your AI systems scale up, are fault tolerant, and are well documented.
Module 1 - Scaling experiments in the cloud
EC2, AMIs & SSH
Module 2 - Managed solutions
Architecting an end-to-end ML product on AWS
Module 3 - Intro to MLOps
ETL, feature stores & data versioning
Module 4 - Custom solutions
Module 5 - Fault-tolerant deployment & autoscaling
CI/CD with GitHub actions
"By the time your graduate, you'll know everything a data scientist or machine learning engineer will need to have an impact in the workplace."
Evgeny Dyshlyuk - Research Scientist, Imperial College London
Data science and machine learning are about understanding how data can be used to make key business decisions and automate processes. Given the huge amounts of data being collected in the digital age, companies across all fields want to utilise their data to inform their decisions and improve operational efficiency. The demand for data scientists has tripled over the past 5 years. The number of machine learning engineer positions on Indeed quadrupled between 2015 and 2018.
This course was created with the intention of helping meet that demand. Most of our students have a STEM background and are required to have a basic understanding of linear algebra, statistics and coding. The 15 minute quiz you complete during the application process will assess this and will give you access to precourse material to fill in any gaps in knowledge you may have.
If you love solving problems across different fields using data and are looking to get hired doing this, you have come to the right place.
Throughout the programme, you will build a portfolio of projects that will showcase your practical skills. But we know there's more to getting hired than technical knowledge.
At the start of the programme you will have a consultation with one of our coaches to help figure out the optimal career for you. As you progress in your learning, our career coaches will help you polish your CV, hold mock interviews, audit your LinkedIn and keep you accountable in your job search process.
You will be recommended to exclusive roles with our hiring partners and be matched with an industry mentor to make sure you are ready for the workplace.
Our goal is for you to feel 100% confident going into any hiring process.
We connect our students to world class AI industry mentors. They’ll lecture technical topics in class, answer questions and share informal career advice in scheduled office hours.
Dedicated support means that on top of the 12 hours in class per week, you’ll have scheduled group office hours weekly, support through Slack and 1-on-1 sessions available to book.
Don’t waste a second. Learn from the comfort of your own home. Reach instructors instantly. Be ready with just an internet connection and your laptop.
Daniel started out as an analyst over 10 years ago now. He quickly transitioned into data science, taking on various roles and honing his skills for 4 years.
He has since grown a passion for teaching, and has been delivering data science education for the past 3 years.
Nihir is a talented NLP specialist, starting his journey many years ago with his bachelors in Computer Science and masters in Data Processing.
He is currently pursuing his PhD in Natural Language Processing at Imperial College London, in
Dr Lucia Specia's Multimodal AI lab.
Szymon is our very own AI prodigy. He is a voracious coder and contributor to the coding community.
Over 408,000 people have read his answers to Python and machine learning questions on Stack Overflow (the Quora of coding). This puts him in the top 0.4% of contributors in the world!
He has written many open source libraries, which over 1000 people have starred on Github.
Having contracted in machine learning for over 10 companies across 4 years, Ali knows all about how machine learning is deployed at a wide variety of companies.
He is passionate about education, having taught over 1200 students over the years.
© Core AI Limited 2020