├── .gitignore
└── README.md


/.gitignore:
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  1 | # Course specific files
  2 | instructor/
  3 | *instructor*
  4 | 
  5 | # Byte-compiled / optimized / DLL files
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132 | # Pyre type checker
133 | .pyre/
134 | 


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/README.md:
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 1 | <center><h2>Introduction to Machine Learning in scikit-learn</h2></center>
 2 | <center><img src="https://imgs.xkcd.com/comics/machine_learning.png" style="width: 40%"/></center>
 3 | 
 4 | > "It's tough to make predictions, especially about the future."   
 5 | > – Yogi Berra   
 6 | 
 7 | ----
 8 | Course Description
 9 | ----
10 | 
11 | In this course, you'll create end-to-end solutions to machine learning problems. This course will cover popular applied techniques in both supervised and unsupervised machine learning, such as regression, classification, and clustering. You'll learn how to properly engineer features, apply algorithms, and evaluate model performance. The focus of the course will be Python's [scikit-learn](https://scikit-learn.org) library.  
12 | 
13 | ----
14 | Logistics
15 | ----
16 | 
17 | __Instructor:__ Brian Spiering   
18 | 
19 | Prerequisites
20 | ----
21 | 
22 | - Working knowledge of probability and statistics.
23 | - Introductory knowledge of linear algebra (e.g., determinants and singular value decomposition).
24 | - Intermediate level of Python (e.g., ability to create to classes).
25 | - No previous knowledge of machine learning required.
26 | 
27 | Learning Outcomes
28 | -----
29 | 
30 | By the end of the course, you should be able to:     
31 | 
32 | 1. Build end-to-end machine learning systems to answer meaningful Data Science questions.
33 | 1. Write idiomatic code in Python's scikit-learn package to model data.
34 | 1. Recognize when to and _when not to_ apply machine learning techniques.
35 | 1. Complete data science take-home challenges that you might encounter during job interviews.
36 | 
37 | -----
38 | Course Topics 
39 | -----
40 | 
41 | 1. Welcome
42 | 1. Machine learning workflow
43 | 1. Scikit-learn API Overview (Estimators, Transformers, Pipelines)
44 | 1. Build your first ML model
45 | 1. Preprocessing
46 | 1. Feature extraction
47 | 1. Feature selection
48 | 1. Principal Component Analysis (PCA)
49 | 1. Model Selection
50 | 1. Classifiers (binary classification and mutliclass classification)
51 | 1. Handling class imbalance with with SMOTE resampling
52 | 1. Classification Metrics
53 | 1. Ensembling
54 | 1. Feature Importance
55 | 1. Creating custom classes in scikit-learn
56 | 1. Clustering
57 |     
58 | Assignments
59 | -----
60 | 
61 | There are five hand-ons assignments to practice applying course concepts to real-world data.
62 | 
63 | Final Project
64 | ------
65 | 
66 | There is a final project where you choose a dataset and complete a end-to-end machine learning project.
67 | 


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