Python for Machine Learning
You will learn how to use data science and machine learning with Python.
Understand Machine Learning from top to bottom.
Learn NumPy for numerical processing with Python.
Conduct feature engineering on real world case studies.
Learn Pandas for data manipulation with Python.
Create supervised machine learning algorithms to predict classes.
Create regression machine learning algorithms for predicting continuous values.
Construct a modern portfolio of machine learning resume projects.
Learn how to use Scikit-learn to apply powerful machine learning algorithms.
Get set-up quickly with the Anaconda data science stack environment.
Understand the full product workflow for the machine learning lifecycle.
Explore how to deploy your machine learning models as interactive APIs.
- Jupyter notebooks
- Machine Learning concepts
- Supervised vs Unsupervised Learning
- Types of Machine Learning – Classification vs Regression
- Machine Learning Methods – All in Theory and Practice
- Linear Regression
- Logistic Regression
- K Nearest Neighbors
- Support Vector Machine
- Decision Trees
- Unsupervised Learning Methods
- Feature Engineering and Data Preparation
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