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Machine Learning Scientist with Python

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.

Python93 hours23 Courses

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Loved by learners at thousands of companies

1

Supervised Learning with scikit-learn

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

4 hours

2

Unsupervised Learning in Python

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

4 hours

3

Linear Classifiers in Python

In this course you will learn the details of linear classifiers like logistic regression and SVM.

4 hours

4

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.

5 hours

5

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.

4 hours

6

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.

4 hours

7

Dimensionality Reduction in Python

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

4 hours

8

Preprocessing for Machine Learning in Python

In this course you'll learn how to get your cleaned data ready for modeling.

4 hours

9

Machine Learning for Time Series Data in Python

This course focuses on feature engineering and machine learning for time series data.

4 hours

10

Feature Engineering for Machine Learning in Python

Create new features to improve the performance of your Machine Learning models.

4 hours

11

Model Validation in Python

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

4 hours

12

Machine Learning Fundamentals in Python

13

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.

4 hours

14

Feature Engineering for NLP in Python

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

4 hours

15

Introduction to TensorFlow in Python

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

4 hours

16

Introduction to Deep Learning in Python

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

4 hours

17

Introduction to Deep Learning with Keras

Learn to start developing deep learning models with Keras.

4 hours

18

Advanced Deep Learning with Keras

Build multiple-input and multiple-output deep learning models using Keras.

4 hours

19

Image Processing in Python

Learn to process, transform, and manipulate images at your will.

4 hours

20

Image Processing with Keras in Python

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

4 hours

21

Hyperparameter Tuning in Python

Learn to tune hyperparameters in Python.

4 hours

22

Introduction to PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

4 hours

23

Machine Learning with PySpark

Learn how to make predictions with Apache Spark.

4 hours

24

Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

4 hours

Instructors

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).