Machine Learning Scientist with Python
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Career track
Master the essential skills to land a job as a machine learning scientist! You'll augment your Python programming skill set with the toolbox to perform supervised, unsupervised, and deep learning. You'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. In the process, you'll get an introduction to natural language processing, image processing, and popular libraries such as Spark and Keras.
PythonClock93 hoursLearn23 Courses
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
4 hours
Hugo Bowne-Anderson
Data Scientist at DataCamp
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
4 hours
Benjamin Wilson
Director of Research at lateral.io
In this course you will learn the details of linear classifiers like logistic regression and SVM.
4 hours
Mike Gelbart
Instructor, the University of British Columbia
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
5 hours
Elie Kawerk
Data Scientist at Mirum Agency
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
4 hours
Sergey Fogelson
VP of Analytics and Measurement Sciences, Viacom
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In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
4 hours
Learn to reduce dimensionality in Python.
4 hours
In this course you'll learn how to get your cleaned data ready for modeling.
4 hours
This course focuses on feature engineering and machine learning for time series data.
4 hours
Create new features to improve the performance of your Machine Learning models.
4 hours
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
4 hours
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
4 hours
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
4 hours
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
4 hours
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
4 hours
Learn to start developing deep learning models with Keras.
4 hours
Build multiple-input and multiple-output deep learning models using Keras.
4 hours
Learn to process, transform, and manipulate images at your will.
4 hours
Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.
4 hours
Learn to tune hyperparameters in Python.
4 hours
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
4 hours
Learn how to make predictions with Apache Spark.
4 hours
Learn how to approach and win competitions on Kaggle.
4 hours
Hugo Bowne-AndersonData Scientist at DataCamp
Benjamin WilsonDirector of Research at lateral.io
Mike GelbartInstructor, the University of British Columbia
Elie KawerkData Scientist at Mirum Agency
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