The UK's fastest growing AI & Data career accelerator program can be summarized in 3 sentences: The AiCore Programme offers software engineering, data science, data analysis, data engineering and machine learning specializations. It provides over 500 hours of coding experience, an internal job board for top industry roles, and support to become an irresistible candidate for your specialist career. Common professions for alumni include data analyst, data engineer, machine learning engineer, and software developer.

1. The UK’s fastest growing Ai & Data career
accelerator

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3. The AiCore Programme Summarised
What You’ll Learn
Software Cloud Engineering Essentials
Data Science Specialism
Data Analysis Specialism
Data Engineering Specialism
Machine learning Engineering Specialism
Building Your Portfolio with AiCore Scenarios
Take the Next Step in Your Career
What Our Alumni say
Learning Packages
Success Stories
Admissions Process

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7. DataScienceSpecialism
Airbnb’sMultimodalPropertyIntelligenceSystem
Airbnb deploys a multimodal neural network AI system to understand listings that are uploaded by
hosts. Multimodal means that it processes various forms (modalities) of data including images
and text descriptions. At Airbnb this system is used to improve search rankings, predict
complaints, and detect fraud. It’s a challenging problem which will require you to do advanced
data manipulation and feature engineering. One of the primary challenges is building this system
in a way that has the potential to scale up to train on a large dataset. Once you’ve developed it,
you’ll deploy it to serve predictions through an API.
Data Cleaning and Exploratory Data AnalysiP
x Data visualisatioi
x Multicolinearitf
x Influential points - Leverages and Outliers
Classificatioi
x Binary Classification„
x Multiclass Classification„
x Multilabel Classification
EnsembleP
x Ensembles„
x Random Forests and Bagging„
x Boosting and Adaboost„
x Gradient Boosting„
x XGBoost
Neural NetworkP
x Neural networks„
x Dropout„
x Batch Normalisation„
x Optimisation for deep learning„
x Convolutional Neural Networks (CNNs)„
x ResNets
Theorf
x Maximum LikelihoodEstimation„
x Evaluation Metrics
Popular Supervised ModelP
x -Nearest Neighbours„
x Classification Trees„
x Support ector Machines„
x Regression Trees
Introduction to machine learnin
x Data for ME
x Intro to models - Linear Regression„
x alidation and Testing„
x Gradient Based Optimisation„
x Bias and ariance„
x yperparameters4 Grid Search and -Fold
Cross alidation
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8. DataAnalyticsSpecialism
MigratingSkyscanner'sDatatoTableau
Take on the role of a Data Analyst at Skyscanner and work on a project requirement to migrate
existing data analytics tasks from an Excel-based manual system into interactive Tableau reports.
This is a widely experienced scenario, which will prepare you for the job before you land it, and
get you hands on practice with the key tools that data analysts need. It will require you to have an
understanding of how data is stored and analysed using traditional and cutting edge tools, and
will challenge you to implement intuitive visualisations of a complex, real-world dataset. At the
end of the day, you’ll have built an analytics engine which could empower hundreds of teams
across a company to provide more value for their business.
Data wrangling and cleaninM
i Data loading_
i Data cleaning_
i Data integration_
i Data exporting
Integrate Tableau Desktop with PostgreSQL RDk
i Setting up Tableau Desktop“
i Configuring PostgreSQL connector“
i Connecting to databases
Create Tableau Report¦
i Tableau data exploration“
i Data analysis and visualisation“
i Creating reports
PostgreSQL RDS Data Import and ReportinM
i Connecting to pgAdming4“
i Creating databases and tables“
i Importing data“
i Data exploration and statistical analysis
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9. DataEngineeringSpecialism
Pinterest'sExperimentationDataEngineeringPipeline
Pinterest has world-class machine learning engineering systems. They have billions of user
interactions such as image uploads or image clicks which they need to process every day to
inform the decisions to make. In this project, you’ll build the system in the cloud that takes in
those events and runs them through two separate pipelines. One for computing real-time metrics
(like profile popularity, which would be used to recommend that profile in real-time), and another
for computing metrics that depend on historical data (such as the most popular category this
year).
Big data engineering foundationL
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{ The Data Engineering Landscape and Lifecycli
{ Data PipelineL
{ Data Ingestion and Data Storagi
{ Enterprise Data WarehouseL
{ Batch vs Real-Time Processin
{ Structured,Unstructured and Complex Data
Data wrangling and transformatios
{ Data Transformations: ELT ET¤
{ Apache Spark and Pyspar¦
{ Distributed Processing with Spar¦
{ Integrating Spark Kafk¬
{ Integrating Spark AWS S–
{ Spark Streaming
Data storagi
{ AWS EssentialL
{ The AWS CLI and Python SDK (boto3¾
{ Data Lake Storage on AWS S–
{ Psycopg2 and SQLAlchem½
{ PgAdmin4
Data orchestratios
{ Apache Airfloî
{ Integrating Airflow Spark
Data managemenô
{ Data governance
{ Data uality
{ Reference data management
{ etadata management
{ Challenges and risks
{ Data fabric
{ Data uality and cleaning
{ Data enrichment
{ Big data privacy and security
Data ingestios
{ Principles of Data Ingestios
{ Batch Processin
{ Real-Time Data Processin
{ APIs and Re uestL
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10. Machine Learning Engineering Specialism
Facebook's Marketplace Product Ranking Algorithm
Use transfer learning on a pre-trained ResNet-50 neural network
to build computer vision and NLP models that process images
and descriptions then combine into the multimodal transformer
architecture Facebook uses to rank search results on the
marketplace. Deploy those models at the scale of Faceook using
Kubernetes.
Introduction to machine learninN
r Data for Mu
r Intro to models - Linear Regressioo
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r Bias and Variancx
r Hyperparameters, Grid Search and K-
Fold Cross Validation Neural Network‰
r Neural networks©
r DropoutBatch Normalisation©
r Optimisation for deep learning©
r Convolutional Neural Networks
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r ResNets
Practica²
r Architecture, data augmentation
debugging tip‰
r Pre-trained model‰
r Transfer learninN
r Hardware acceleration (GPUs TPUs)
PyTorcÛ
r Automatic differentiation©
r PyTorch Datasets and
DataLoaders©
r Making custom datasets
Building API‰
r Intro to FastAPIì
r Deploying FastAPIì
r Efficient FastAPI
Classificatioo
r Binary Classification©
r Multiclass Classification©
r Multilabel Classification
Application‰
r Churn ModellinN
r FAISS Vector SearcÛ
r Image Based Search
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16. 14 day money back guarantee
Aside from that, we are certain you will love our programme but if you decide
up to 2 weeks into the programme that AiCore isn't right for you, simply
withdraw with no penalty and get an instant refund.

17. $
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