Explanation
Machine Learning Scientist with Python, Learn essential Python skills to get a job as a machine learning scientist. In this way, you will get a complete introduction to Python machine learning. You’ll expand on your existing Python programming skills by using the tools needed to implement supervised, supervised, and in-depth learning. You’ll learn how to process feature data, train your models, evaluate performance, and set good performance metrics. This track also covers topics such as tree-based machine learning models, cluster analysis, preparation for machine learning, and more. By the time you’re done, you’ll have the confidence to use Python for machine learning, working with real data, linear classifiers, gradient boosting, and more. Along the way, you’ll get an introduction to natural language processing, image processing, and popular Python machine learning packages like scikit-learn, Spark, and Keras.
What will you learn?
- supervised Learn with scikit-bar
- Without protection Learning Python
- straight Python classifiers
- the machine Learning Tree-Based Patterns in Python
- excessive Gradient Boosting in XGBoost
- in size Python reduction
- Making an introduction Machine Learning in Python
- the machine Learning Time Series Data in Python
- Video Machine Learning Engineering in Python
- The model Validating Python
- Introduction Natural Language Programming with Python
- Video NLP Engineering in Python
- Introduction To TensorFlow in Python
- Introduction Keras deep learning
- Image Working with Python
- Image Working with Keras in Python
- Hyperparameter Installing Python
- Introduction PySpark
- the machine Learning PySpark
Specificatoin for a Machine Learning Scientist with Python
Essentials of a Machine Learning Scientist with Python
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 720p
This course consists of 23 courses
Download Links
Supervised learning with scikit-learn
Download – 87 MB
Unsupervised learning in Python
Download – 117 MB
Linear classifiers in Python
Download – 79 MB
Machine Learning for Tree-Based Models in Python
Download – 88 MB
XGBoost is growing slowly
Download – 147 MB
Cluster Analysis in Python
Download – 66 MB
Dimension reduction in Python
Download – 81 MB
Preparing for Machine Learning in Python
Download – 52 MB
Machine Learning for Time Series Data in Python
Download – 83 MB
Engineering techniques for Machine Learning in Python
Download – 132 MB
Model Verification in Python
Download – 65 MB
An Introduction to Natural Language Processing in Python
Download – 95 MB
Engineering techniques for NLP in Python
Download – 73 MB
Introduction to TensorFlow in Python
Download – 85 MB
An Introduction to Deep Learning in Python
Download – 133 MB
Introduction to Deep Learning with Keras
Download – 88 MB
Higher Education in Keras
Download – 132 MB
Image Processing in Python
Download – 132 MB
Working with Keras image in Python
Download – 162 MB
Hyperparameter Tuning in Python
Download – 73 MB
Introduction to PySpark
Download – 273 KB
Machine Learning in PySpark
Download – 105 MB
Winning the Kaggle competition in Python
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file size
2.1 GB