What is Unique Corporate Training?
In-person training delivered at your office by an industry expert.
Highly practical course materials focused on the workflow of a Data Scientist including:
- Interactive Python notebooks with exercises and solutions
- Continuous learning projects to complete using K.A.T.E.®
Cover the most industry-relevant skills and cutting-edge techniques in Data Science that can be applied to:
a) Make better decisions faster
b) Lower costs with increased operational efficiency
c) Increase revenue by developing new product offerings
Python Courses include:
Pragmatic Python Programming (2 day course) -
Python fundamentals, interacting with APIs, data processing and code architecture
Python for Analysts (2 day course) -
Intensive conversion training courses for developers writing and maintaining Python projects in Production.
Python for Engineers (2 day course) -
Intensive Python conversion training course for developers familiar with C
Data Science courses include:
Core Data Science (3 day course) -
Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning
Data Science in Production (3 day course) -
Preprocessing, Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning, model evaluation and tuning, random forests and logistic regression.
Machine Learning and Deep Learning (2 day course) -
Model evaluation, decision trees, random forests, logistic regression, SVMs, neural networks and deep learning
Apache Spark in Practice (2 day course) -
Learn how to work with Spark (RDDs, Dataframes and SQL) and Parquet
Pragmatic Model Interpretation (1 day course) -
LIME, SHAP, ELI5 and best practices for interpreting and evaluating machine learning models
Regression and Time Series Analysis (2 day course) -
Linear regression, nonlinear regression, auto-regressive models, time series analysis and regularisation
Specialised Data Science courses include:
Cloud Computing and Databases (2 day course) -
Th course covers working SQL to query structured data and NoSQL. In particular, the second focuses on document-oriented storage and graph databases.
Neural Networks and Deep Learning (2 day course) -
This course is designed to ensure your team gains a strong understanding of how neural networks are constructed and how to apply them using Keras.
Highly practical course materials focused on the workflow of a Data Scientist including:
- Interactive Python notebooks with exercises and solutions
- Continuous learning projects to complete using K.A.T.E.®
Cover the most industry-relevant skills and cutting-edge techniques in Data Science that can be applied to:
a) Make better decisions faster
b) Lower costs with increased operational efficiency
c) Increase revenue by developing new product offerings
Python Courses include:
Pragmatic Python Programming (2 day course) -
Python fundamentals, interacting with APIs, data processing and code architecture
Python for Analysts (2 day course) -
Intensive conversion training courses for developers writing and maintaining Python projects in Production.
Python for Engineers (2 day course) -
Intensive Python conversion training course for developers familiar with C
Data Science courses include:
Core Data Science (3 day course) -
Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning
Data Science in Production (3 day course) -
Preprocessing, Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning, model evaluation and tuning, random forests and logistic regression.
Machine Learning and Deep Learning (2 day course) -
Model evaluation, decision trees, random forests, logistic regression, SVMs, neural networks and deep learning
Apache Spark in Practice (2 day course) -
Learn how to work with Spark (RDDs, Dataframes and SQL) and Parquet
Pragmatic Model Interpretation (1 day course) -
LIME, SHAP, ELI5 and best practices for interpreting and evaluating machine learning models
Regression and Time Series Analysis (2 day course) -
Linear regression, nonlinear regression, auto-regressive models, time series analysis and regularisation
Specialised Data Science courses include:
Cloud Computing and Databases (2 day course) -
Th course covers working SQL to query structured data and NoSQL. In particular, the second focuses on document-oriented storage and graph databases.
Neural Networks and Deep Learning (2 day course) -
This course is designed to ensure your team gains a strong understanding of how neural networks are constructed and how to apply them using Keras.
Participants will learn about neural network architectures, ranging from simple single-layer architectures to deep learning architectures including CNNs and RNNs.
Natural Language Processing (2 day course) -
This course is designed to provide your team with hands-on training in text processing, semantic analysis and sophisticated Machine Learning approaches.
Recommender Systems, Interpretability and Evaluation (2 day course) -
This course is split into three core sections, building Recommender Systems, followed by model evaluation and interpretability. We cover different types of Recommender Systems such as Collaborative Recommender systems, Content-based recommender systems and Hybrid recommender systems..
Natural Language Processing (2 day course) -
This course is designed to provide your team with hands-on training in text processing, semantic analysis and sophisticated Machine Learning approaches.
Recommender Systems, Interpretability and Evaluation (2 day course) -
This course is split into three core sections, building Recommender Systems, followed by model evaluation and interpretability. We cover different types of Recommender Systems such as Collaborative Recommender systems, Content-based recommender systems and Hybrid recommender systems..
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