Duration
Introduction to Machine Learning with Python
5 days
Overview
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. This course will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
In this course, you will learn.
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms.
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques.
- Suggestions for improving your machine learning and data science skills.
Introduction
- Why Machine Learning?
- Problems Machine Learning Can Solve
- Knowing Your Task and Knowing Your Data
- Why Python?
- Scikit-learn
- Essential Libraries and Tools
- Jupyter Notebook
- NumPy
- SciPy
- Matplotlib
- Pandas
- mglearn
- A First Application: Classifying Iris Species
- Supervised Learning
- Classification and Regression
- Generalization, Overfitting and Underfitting
- Supervised Machine Learning Algorithms
- Uncertainty Estimates from Classifiers
- Unsupervised Learning and Preprocessing
- Types of Unsupervised Learning
- Challenges in Unsupervised Learning
- Preprocessing and Scaling
- Dimensionally Reduction, Feature Extraction, and Manifold Learning
- Clustering
- Representing Data and Engineering Features
- Categorical Variables
- OneHotEncoder and ColumnTransformer: Categorical Variables with scikit-learn
- Convenient ColumnTransformer creation with make_columntransformer
- Binning, Discretization, Linear Models and Trees
- Interactions and Polynomials
- Univariate Nonlinear Transformation
- Automatic Feature Selection
- Model Evaluation and Improvement
- Cross-Validation
- Grid Search
- Evaluation Metrics and Scoring
- Algorithm Chains and Pipelines
- Parameter Selection with Preprocessing
- Building Pipelines
- Using Pipelines in Grid Searches
- The General Pipeline Interface
- Grid-Searching Preprocessing Steps and Model Parameters
- Grid-Searching Which Model to Use
- Working with Text Data
- Types of Data Represented as Strings
- Example Application: Sentiment Analysis of Movie Reviews
- Representing Text Data as a Bag of Words
- Stopwords
- Rescaling the Data with tf-idf
- Investigating Model Coefficients
- Advanced Tokenization, Stemming and Lemmatization
- Wrapping Up
- Approaching a Machine Learning Problem
- From Prototype to Production
- Testing Production Systems
- Building Your Own Estimator
- Where to Go from Here
Programme scope, delivery arrangements and suitability are confirmed during enquiry.