Description
Machine Learning Scientist with Python, Data Science Scientist training course with Python is published by Datacamp Academy. Learn the essential Python skills to work as a machine learning scientist. With this track, you will get a comprehensive introduction to machine learning in Python. You’ll enhance your current Python programming skill set with the tools you need to perform supervised, unsupervised, and deep learning.
You will learn how to process data for features, train your models, evaluate 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. When you’re done, you’ll feel confident using Python for machine learning, working with real data sets, linear classifiers, gradient boosting, and more. In this article, you will learn about natural language processing, image processing, and popular Python machine learning packages such as Scikit-Learning, Spark, and Keras.
What you will learn
- Unsupervised learning in Python
- Linear classifiers in Python
- Machine learning with tree-based models in Python
- Gradient boosting with XGBoost
- Cluster analysis in Python
- Dimension reduction in Python
- Preprocessing for Machine Learning in Python
- Machine learning for time series data in Python
- Feature engineering for machine learning in Python
- Model Validation in Python
- An introduction to natural language processing in Python
- Feature engineering for NLP in Python
- Introducing tensorflow in Python
- Familiarity with deep learning in Python
- An introduction to deep learning with CROSS
- Image processing in Python
- Image processing with Cress in Python
- Hyperparameter tuning in Python
- Introducing PySpark
- Machine learning with PySpark
Details of the Machine Learning Scientist with Python course
- Publisher: Datacamp
- teacher : George Boorman , Richie Cotton
- English language
- Education level: all levels
- Number of courses: 23
- Duration of training: 93 hours to complete the course (the time period mentioned on the reference site is for completing the course and acquiring skills along with exercises and… for more information, refer to the reference site)
Chapters of the Machine Learning Scientist with Python course
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Supervised learning with scikit-learn
Download – 87 MB
Unsupervised Learning in Python
Download – 117 MB
Linear Classifiers in Python
Machine Learning with Tree-Based Models in Python
Download – 88 MB
Extreme Gradient Boosting with XGBoost
Download – 147 MB
Cluster Analysis in Python
Download – 66 MB
Dimensionality Reduction in Python
Download – 81 MB
Preprocessing for Machine Learning in Python
Download – 52 MB
Machine Learning for Time Series Data in Python
Download – 83 MB
Feature Engineering for Machine Learning in Python
Download – 132 MB
Model Validation in Python
Download – 65 MB
Introduction to Natural Language Processing in Python
Download – 95 MB
Feature Engineering for NLP in Python
Download – 73 MB
Introduction to TensorFlow in Python
Download – 85 MB
Introduction to Deep Learning in Python
Download – 133 MB
Introduction to Deep Learning with Keras
Download – 88 MB
Advanced Deep Learning with Keras
Download – 132 MB
Image Processing in Python
Download – 132 MB
Image Processing with Keras in Python
Download – 162 MB
Hyperparameter Tuning in Python
Download – 73 MB
Introduction to PySpark
Download – 237 KB
Machine Learning with PySpark
Winning a Kaggle Competition in Python
File(s) password: www.downloadly.ir
Size
2.1 GB
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