Machine Learning with Python: Foundations

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Frederick Nwanganga

1:43:35

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01 - Introduction

01 - Machine learning in our world.mp4

00:50

02 - What you should know.mp4

00:45

03 - The tools you need.mp4

01:02

04 - Using the exercise files.mp4

01:18

02 - Machine Learning

01 - What is machine learning.mp4

03:59

02 - What is not machine learning.mp4

03:55

03 - What is unsupervised learning.mp4

02:39

04 - What is supervised learning.mp4

04:05

05 - What is reinforcement learning.mp4

06:06

06 - What are the steps to machine learning.mp4

05:13

03 - Collecting Data for Machine Learning

01 - Things to consider when collecting data.mp4

03:48

02 - How to import data in Python.mp4

05:03

04 - Understanding Data for Machine Learning

01 - Describe your data.mp4

03:33

02 - How to summarize data in Python.mp4

06:00

03 - Visualize your data.mp4

03:42

04 - How to visualize data in Python.mp4

06:23

05 - Preparing Data for Machine Learning

01 - Common data quality issues.mp4

03:42

02 - How to resolve missing data in Python.mp4

07:34

03 - Normalizing your data.mp4

04:39

04 - How to normalize data in Python.mp4

04:38

05 - Sampling your data.mp4

04:07

06 - How to sample data in Python.mp4

06:35

07 - Reducing the dimensionality of your data.mp4

03:24

06 - Types of Machine Learning Models

01 - Classification vs. regression problems.mp4

03:15

02 - How to build a machine learning model in Python.mp4

05:55

07 - Conclusion

01 - Next steps with applied machine learning.mp4

01:25

Exercise Files

Ex_Files_ML_with_Python_Foundations.zip

Description

You’ve probably heard about machine learning before, but have you ever wondered what that term really means? How does a machine learn? Have you thought about building a machine learning model, but didn’t know where to start? In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then gets into how to collect, understand, and prepare data for machine learning. He also provides guided examples of how to accomplish each step using Python. Finally, he brings it all together to build, evaluate, and interpret the results of a machine learning model in Python.

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Python

Machine Learning

Frederick Nwanganga

Instructor's Courses

Information Technology leader with over 15 years of experience in both private sector and higher education. Skilled in managing highly talented and technical staff. Subject matter competence in Data Analytics, Machine Learning and Optimizations for Big Data Applications.

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