Machine Learning with Scikit-learn
Instructor: Safdar Munir
5.0
Course Duration:40 Hours
Course level:Intermediate
What I will learn?
- Understand and apply supervised learning principles, distinguishing between classification and regression problems
- Gain hands-on expertise using scikit-learn for implementing algorithms, evaluating models, and fine-tuning through metrics and hyperparameter tuning.
- Apply machine learning to practical problems, master data preprocessing techniques, and efficiently use pipelines for both classification and regression tasks
- Proficiency in techniques such as handling missing data, creating dummy variables, and scaling data
About Course
Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam?
In this course, you’ll learn how to use Python to perform supervised learning, an essential component of machine learning. You’ll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets.
You’ll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.
Course Curriculum
In this chapter, you will be introduced to classification problems and learn how to solve them using supervised learning techniques. And you’ll apply what you learn to a different dataset.
- Supervised learning
- Which of these is a classification problem?
- Exploratory data analysis
- Numerical EDA
- Visual EDA
- The classification challenge
- k-Nearest Neighbors: Fit
- k-Nearest Neighbors: Predict
- Measuring model performance
- The digits recognition dataset
- Train/Test Split + Fit/Predict/Accuracy
- Overfitting and underfitting
In the previous chapter, you used image and different datasets to predict binary and multiclass outcomes. But what if your problem requires a continuous outcome? Regression is best suited to solving such problems. You will learn about fundamental concepts in regression and apply them to predict the life expectancy
- Introduction to regression
- Which of the following is a regression problem?
- Importing data for supervised learning
- Exploring the data
- The basics of linear regression
- Fit & predict for regression
- Train/test split for regression
- Cross-validation
- 5-fold cross-validation
- K-Fold CV comparison
- Regularized regression
- Regularization I: Lasso
- Regularization II: Ridge
- How good is your model?
- Metrics for classification
- Logistic regression and the ROC curve
- Building a logistic regression model
- Plotting an ROC curve
- Precision-recall Curve
- Area under the ROC curve
- AUC computation
- Hyperparameter tuning
- Hyperparameter tuning with GridSearchCV
- Hyperparameter tuning with RandomizedSearchCV
- Hold-out set for final evaluation
- Hold-out set reasoning
- Hold-out set in practice I: Classification
- Hold-out set in practice II: Regression
This chapter introduces pipelines, and how scikit-learn allows for transformers and estimators to be chained together and used as a single unit. Preprocessing techniques will be introduced as a way to enhance model performance, and pipelines will tie together concepts from previous chapters.
- Preprocessing data
- Exploring categorical features
- Creating dummy variables
- Regression with categorical features
- Handling missing data
- Dropping missing data
- Imputing missing data in a ML Pipeline I
- Imputing missing data in a ML Pipeline II
- Centering and scaling
- Centering and scaling your data
- Centering and scaling in a pipeline
- Bringing it all together I: Pipeline for classification
- Bringing it all together II: Pipeline for regression
- Final thoughts
Requirements
- Prior exposure to basic machine learning concepts would be beneficial but not mandatory.
- Basic understanding of Python programming language.
- Dedication to completing assignments, exercises, and projects
- Time commitment for watching lectures and engaging in hands-on practice
Material Includes
- Code Examples and Jupyter Notebooks
- Datasets
- Reading Materials
- Assignments and Projects
- Quizzes and Assessments
- Discussion Forums or Q&A Sessions
- Feedback Mechanism
- Support and Guidance
