Instructor-led training

Introduction to Machine Learning with Python Training in Portland, Maine

Machine Learning training from Hartmann Software Group, delivered onsite in Portland, Maine or live online.

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What you'll learn

This course employs many advanced Python libraries to provide the student with a solid foundation of Machine Learning concepts and practices.

Prerequisites

Python proficient.

Course Outline

Introduction

Why Machine Learning?

Problems Machine Learning Can Solve

Knowing Your Task and Knowing Your Data

Advanced Python Basics

scikit-learn

Installing scikit-learn

Essential Libraries and Tools

Jupyter Notebook

NumPy

SciPy

matplotlib

pandas

mglearn

A First Application: Classifying Iris Species 13

Meet the Data

Measuring Success: Training and Testing Data

First Things First: Look at Your Data

Building Your First Model: k-Nearest Neighbors

Making Predictions

Evaluating the Model

Supervised Learning

Classification and Regression

Generalization, Overfitting, and Underfitting

Relation of Model Complexity to Dataset Size

Supervised Machine Learning Algorithms

Some Sample Datasets

k-Nearest Neighbors

Linear Models

Naive Bayes Classifiers

Decision Trees

Ensembles of Decision Trees

Kernelized Support Vector Machines

Neural Networks (Deep Learning)

Uncertainty Estimates from Classifiers

The Decision Function

Predicting Probabilities

Uncertainty in Multiclass Classification

Unsupervised Learning and Preprocessing

Types of Unsupervised Learning

Challenges in Unsupervised Learning

Preprocessing and Scaling

Different Kinds of Preprocessing

Applying Data Transformations

Scaling Training and Test Data the Same Way

The Effect of Preprocessing on Supervised Learning

Dimensionality Reduction, Feature Extraction, and Manifold Learning

Principal Component Analysis (PCA)

Non-Negative Matrix Factorization (NMF)

Manifold Learning with t-SNE

Clustering

k-Means Clustering

Agglomerative Clustering

DBSCAN

Comparing and Evaluating Clustering Algorithms

Representing Data and Engineering Features

Categorical Variables

One-Hot-Encoding (Dummy Variables)

Numbers Can Encode Categoricals

Binning, Discretization, Linear Models, and Trees

Interactions and Polynomials

Univariate Nonlinear Transformations

Automatic Feature Selection

Univariate Statistics

Model-Based Feature Selection

Iterative Feature Selection

Utilizing Expert Knowledge

Model Evaluation and Improvement

Cross-Validation

Cross-Validation in scikit-learn

Benefits of Cross-Validation

Stratified k-Fold Cross-Validation and Other Strategies

Grid Search

Simple Grid Search

The Danger of Overfitting the Parameters and the Validation Set

Grid Search with Cross-Validation

Evaluation Metrics and Scoring

Keep the End Goal in Mind

Metrics for Binary Classification

Metrics for Multiclass Classification

Regression Metrics

Using Evaluation Metrics in Model Selection

Algorithm Chains and Pipelines

Parameter Selection with Preprocessing

Building Pipelines

Using Pipelines in Grid Searches

The General Pipeline Interface

Convenient Pipeline Creation with make_pipeline

Accessing Step Attributes

Accessing Attributes in a Grid-Searched Pipeline

Grid-Searching Preprocessing Steps and Model Parameters

Grid-Searching Which Model To Use

Training locations

Introduction to Machine Learning with Python training across the U.S. and beyond.

We provide Introduction to Machine Learning with Python training onsite and live online for organizations throughout the United States and internationally.

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