This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!

The Program

1. Course Introduction.
2. Environment Set-Up.
3. Jupyter Overview.
4. Python Crash Course.
5. Python for Data Analysis — NumPy.
6. Python for Data Analysis — Pandas.
7. Python for Data Analysis — Pandas Exercises.
8. Python for Data Visualization — Matplotlib.
9. Python for Data Visualization — Seaborn.
10. Python for Data Visualization — Pandas Built-in Data Visualization.
11. Python for Data Visualization — Plotly and Cufflinks.
12. Python for Data Visualization — Geographical Plotting.
13. Data Capstone Project.
14. Introduction to Machine Learning.
15. Linear Regression.
16. Cross Validation and Bias-Variance Trade-Off.
17. Logistic Regression.
18. K Nearest Neighbors.
19. Decision Trees and Random Forests.
20. Support Vector Machines.
21. K Means Clustering.
22. Principal Component Analysis.
23. Recommender Systems.
24. Natural Language Processing.
25. Neural Nets and Deep Learning.
26. Big Data and Spark with Python.
27. Bonus Section.

What will you learn 

• Use Python for Data Science and Machine Learning.
• Implement Machine Learning Algorithms.
• Learn to use Pandas for Data Analysis.
• Learn to use Seaborn for statistical plots.
• Use SciKit-Learn for Machine Learning Tasks.
• Logistic Regression.
• Random Forest and Decision Trees.
• Neural Networks.
• Use Spark for Big Data Analysis.
• Learn to use NumPy for Numerical Data.
• Learn to use Matplotlib for Python Plotting.
• Use Plotly for interactive dynamic visualizations.
• K-Means Clustering.
• Linear Regression.
• Natural Language Processing and Spam Filters.
• Support Vector Machines.