5 of 5 Random Forest Jobs in England

PySpark DataLake Architecture, AWS Redshift, Kinesis, EMR Machine Learning Supervised Models: Nave Bayes, Logistic Regression, SVM, Linear Regression, KNN, Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost Unsupervised Models: K-Means, DBSCAN, Hierarchical Clustering Deep Learning ANN, CNN, RNN, LSTM Frameworks: TensorFlow (Gradient Tape), PyTorch ...

plus to manipulate data and draw insights from large data sets Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc. Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling ...

Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning) Extensive experience with neural networks, including RNNs, LSTMs, Transformers Expertise in gradient boosting, random forests, and ensemble methods Experience with generative models (GANs, VAEs, Diffusion models) Programming & Tools : Expert-level Python programming Proficiency with ML frameworks (PyTorch, TensorFlow ...

PyData stack (pandas, numpy, scikit-learn, matplotlib etc.). ● Knowledge of machine learning and statistical models, e.g. linear/logistic regression, decision trees, random forest, unsupervised methods etc. ● Basic knowledge of relational databases and SQL. ● Experience conveying complex information through Data Visualisation. Nice to have: ● Experience working with ...

PyData stack (pandas, numpy, scikit-learn, matplotlib etc.). ● Knowledge of machine learning and statistical models, e.g. linear/logistic regression, decision trees, random forest, unsupervised methods etc. ● Basic knowledge of relational databases and SQL. ● Experience conveying complex information through Data Visualisation. Nice to have: ● Experience working with ...