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We turn STEM grads into job-ready candidates

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Data Engineering

Data Science & Machine Learning

Deep Learning

ML Engineering & MLOps

Career Support

Overview

Curriculum

Course schedule

How to apply

Start dates

Instructors

Tuition plans

FAQs

Next cohort start 20th Sep

Apply now

Is this course right for you?

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.

What we need from you

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.

18

Weeks

200+

Hours of coding

6

Portfolio projects

A four-part curriculum to take you
from the basics to the state of the art

Unit 1.

Data Engineering

Data collection, Data formats, Pandas Python library, Data cleaning, SQL, ETL, Distributed computing & Data versioning

Module 1:
Data collection

Introduction to data pipelines
Introduction to APIs
HTTP requests
Selenium for web scraping
Performing basic web automation actions
Referencing HTML elements using Xpath
Cron

Module 2:
Data formats & the Pandas Python library

CSV, JSON & Parquet file formats
Intro to Pandas

Module 3:
Data cleaning

Reasons for and approaches to data cleaning
Handling missing data

Module 4:
Intro to cloud

Data Lakes and Warehouses
AWS S3
AWS DynamoDB
AWS RDS

Module 5:
SQL

SQL basics
SQL Join operations
SQL Aggregations
SQL Subqueries

Module 6:
ETL, Distributed Computing & Data Versioning

Intro to DataBricks
Distributed computing with PySpark
ETL
Data Versioning

Unit 2.

Data Science & Machine Learning

Exploratory data analysis, Intro to Machine Learning, Theory, Supervised Models, Ensembles, Unsupervised learning techniques

Module 1:
Data science fundamentals

Statistical modelling
Hypothesis testing
Multi-collinearity
Influential points

Module 2:
Introduction to machine learning

Intro to ML & when (NOT) to use
Scikit-learn
Validation and testing
Linear regression
Logistic regression
Multiclass classification
Hyperparameters, grid search and K-fold cross validation
Feature selection and feature engineering

Module 3:
Theory

Evaluation metrics
Bias & variance, underfitting and overfittting
Regularisation
Maximum Likelihood Estimation (MLE)

Module 4:
More supervised machine learning models

K-nearest neighbours
Decision trees
Random forests
Gradient boosting

Module 5:
Unsupervised machine learning models

K-means clustering
Dimensionality reduction using principal component analysis (PCA) & T-SNE
DBSCAN

Unit 3.

Deep learning

Neural Networks, Computer Vision Fundamentals, Acceleration, Pretrained models, Real-world Applications

Module 1:
PyTorch

Automatic differentiation
PyTorch Datasets and DataLoaders
Making custom datasets

Module 2:
Neural Networks

Neural networks
Dropout
Batch Normalisation
Optimisation for deep learning
Convolutional Neural Networks (CNNs)
ResNets

Module 3:
Practical

Architecture tips, data augmentation & debugging tips
Pre-trained models
Transfer learning
Hardware acceleration (GPUs & TPUs)

Module 4:
Advanced Architectures

Attention
Self-attention
Transformers

Module 5:
Applications

Churn modelling
Content based recommendation systems
Collaborative filtering
Lead scoring

Unit 4.

ML Engineering & MLOps

Deployment, Automated deployment, CI/CD, scaling, Monitoring & CT

Module 1:
Scaling experiments in the cloud

EC2, AMIs & SSH
Experiment tracking
Ray Tune

Module 2:
Managed Solutions

AWS Sagemaker
Architecting an end-to-end ML product on AWS

Module 3:
Intro to MLOps

MLOps overview
ETL
Feature stores
Data versioning
Orchestration & Airflow

Module 4:
Custom Solutions

Docker
Flask
MLFlow
TorchServe
Prometheus
Grafana

Module 5:
Fault-tolerant deployment and auto-scaling

CI/CD with GitHub actions
Kubernetes
AWS EKS
Kubeflow
AWS Cloudformation

Career support

Our curriculum is approved by
industry professionals

Federico Monti

Senior Machine Learning Engineer @ Twitter

Loïc Genest

Data Scientist @ Expedia

David Aponte

Machine Learning Engineer @ Benevolent AI

Daniel Clarke

Head of applied science @ Adarga

Aparna Dhinakaran

Founder @ Arize AI

Alexey Grigorev

Principal Data Scientist @ OLX Group

Tom Preece

Data Scientist @ Peak

Korri Jones

Senior Machine Learning Engineer @ Chick-fil-A

Jakub Czakon

Senior Data Scientist @ neptune.ai

Peter Bentley

Visiting professor @ Autodesk

Demetrios Brinkmann

Community Coordinator@ MLOps community

Tao Cao

Principal Machine Learning Engineer @ GSK

Vitalii Zhelezniak

Senior Research Scientist @ Babylon Health

Filipe Morais

Machine Learning Engineer @ Onfido

Mohamed Redha Sidoumou

Data Scientist @ AWS

Course schedule

Our course is part-time and runs from Monday - Thursday, 6:30 pm - 9:30 pm

6:30 - 7:30 pm

Lecture

Our expert instructors will walk you through new content and explain the underlying concepts

7:40 - 8:30 pm

Independent work

Work on challenges independently

8:40 - 9:30 pm

Group work

Work with your cohort in small groups

How to apply

Change your future in four easy steps

01

Application Form

Complete the application form in less than 10 minutes

02

Take our quiz

Find out if you are course-ready by taking a short assesment

03

Interview

Talk to our Admissions team to make sure this Course is right for you

04

Orientation

After the evaluation process, you will recieve a decision. Then get started!

Upcoming start dates

20th September 2021

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

18th October 2021

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

22nd November 2021

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

Our industry mentors will make sure you're ready for the workplace

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.

Sam Cooper

Senior Lecturer,
Imperial College London

Felisia Loukou

Senior Data Scientist,
Adarga

Colin Kelly

NLP Lead,
Adarga

Dedicated Instructors

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.

Remote Learning, done right

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.

Our instructors have diverse expertise
Dan Sanz Becerril

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.

Szymon Maszke

Szymon is an open-source prodigy. Over 600k people have read his answers to machine learning questions on Stack Overflow, putting him in the top 0.4% of contributers in the world.

Ali Abdelaal

Having contracted at over 10 companies across 4 years, Ali knows how machine learning is deployed at a wide variety of companies.

He has years of experience in education, having taught over 1200 students.

Harry Berg

Harry is a passionate teacher and hacker. Over the years, he has taught over 1000 students across the top universities in London including Imperial College, UCL and Kings.

You could be a

data scientist.
machine learning engineer.
data analyst.
data engineer.
business analyst.
natural language processing engineer.
data analytics consultant.

Tuition plans that work for you

Your financial background should be no barrier to accessing a quality education. Our wide range of tuition plans are designed to give you ultimate flexibility regardless of your circumstances.

Pay upfront

Pay the cost of the course before the date of your first class through a bank transfer.

AiCore Flagship

Pay the tuition cost in manageable monthly installments over 24 months.

No interest

No deposit

Income contingent plan

Pay tuition cost in monthly installments as a percentage of your salary after you've landed a high paying job.

Still have questions?

You could be our next
success story

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