Machine Learning Scientist with Python
Master the essential Python skills to land a job as a machine learning scientist! With this track, you'll gain a comprehensive introduction to machine learning in Python. You’ll augment your existing Python programming skill set with the tools needed 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. This track also covers topics including tree-based machine learning models, cluster analysis, preprocessing for machine learning, and more. By the time you finish, you’ll have the confidence to use Python for machine learning, working with real data sets, linear classifiers, gradient boosting, and more. In the process, you'll get an introduction to natural language processing, image processing, and popular Python machine learning packages such as scikit-learn, Spark, and Keras.
Python93 hours23 Courses
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Supervised Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Linear Classifiers in Python
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Cluster Analysis in Python
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Dimensionality Reduction in Python
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Preprocessing for Machine Learning in Python
In this course you'll learn how to get your cleaned data ready for modeling.
Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.
Feature Engineering for Machine Learning in Python
Create new features to improve the performance of your Machine Learning models.
Model Validation in Python
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Introduction to Natural Language Processing in Python
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Feature Engineering for NLP in Python
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Introduction to TensorFlow in Python
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Introduction to Deep Learning in Python
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Introduction to Deep Learning with Keras
Learn to start developing deep learning models with Keras.
Advanced Deep Learning with Keras
Build multiple-input and multiple-output deep learning models using Keras.
Image Processing with Keras in Python
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Introduction to PySpark
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Machine Learning with PySpark
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
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