Who should take this course?
Experienced Python developers looking to understand a wide variety of machine learning algorithms, including supervised and unsupervised learning algorithms.
Prerequisites
Introduction to Python
Python for Data Science
The Training Covers These Topics:
- 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.
1 – Python
- Jupyter notebooks
2 – Numpy
3 – Pandas
4 – Matplotlib
5 – Machine Learning concepts
- Supervised vs Unsupervised Learning
- Types of Machine Learning – Classification vs Regression
- Evaluation
6 – Machine Learning Methods – All in Theory and Practice
- Linear Regression
- Logistic Regression
- K Nearest Neighbors
- Support Vector Machine
- Decision Trees
- Unsupervised Learning Methods