Building Machine Learning Models in Python with scikit-learn
This course course will help engineers and data scientists learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. No prior experience with ML needed, only basic Python programming knowledge.
What you'll learn
The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks.
In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models.
First, you will learn how to use libraries for working with continuous, categorical, text as well as image data.
Next, you will get to go beyond ordinary regression models, seeing how to implement specialized regression models such as Lasso and Ridge regression using the scikit-learn libraries. Finally, in addition to supervised learning techniques, you will also understand and implement unsupervised models such as clustering using the mean-shift algorithm and dimensionality reduction using principal components analysis.
At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered and you will be extremely comfortable using the Python scikit-learn library to build and train your models. Software required: scikit-learn, Python 3.x.
Table of contents
- Version Check 0m
- Module Overview 2m
- Prerequisites and Course Overview 3m
- Machine Learning Use Cases and scikit-learn 7m
- Supervised and Unsupervised Learning Techniques 6m
- Demo: Useful Python Packages 1m
- Mean and Variance 5m
- Demo: Scaling Numeric Data 3m
- Categorical Data and One-hot Encoding 2m
- Demo: Representing Categorical Data in Numeric Form 3m
- Representing Text in Numeric Form 4m
- Frequency Based Encoding: Count Vectors 4m
- Frequency Based Encoding: TF/IDF 3m
- Demo: CountVectorizers, TfidfVectorizer, HashingVectorizer 5m
- Representing Images in Numeric Form 3m
- Demo: Extracting Features from Images 5m
- Module Overview 1m
- Ordinary Least Square Regression 3m
- Measuring Fit Using R-squared 5m
- Demo: Data Preparation 7m
- Demo: Linear Regression Using Estimators 5m
- L1 and L2 Norm 3m
- Overfitting and The Bias-variance Trade-off 5m
- Multicollinearity in Regression 4m
- Lasso and Ridge Regression 4m
- Demo: Lasso Regression 4m
- Demo: Ridge Regression 3m
- Support Vector Regression 6m
- Demo: Support Vector Regression 4m
- Demo: SVR Reduced Penalty 2m
- Module Overview 1m
- Support Vector Machines for Classification 3m
- Setting up the SVM Classification Problem 5m
- Demo: SVM Text Classification 5m
- Demo: SVM Image Classification with Grid Search 5m
- Decision Trees 9m
- Random Forests 1m
- Gradient Boosting Regression 4m
- Gradient Boosting Regression and Shrinkage Factor 3m
- Demo: Gradient Boosting Regression with Grid Search 6m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providin... more
