Description

This skill set is in high demand, as machine learning algorithms now power the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.

This course will begin with linear and logistic regression—the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as gain tips for choosing effective features and algorithms.

The course will focus on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal mathematics component. Entering students are expected to be comfortable writing Python programs, as well as using the NumPy and Pandas libraries.

Course Overview

1. Course Kick‑off & Python Refresher

Data Science tool recap - Pandas and indexing
Exploratory data analysis (EDA): standard deviations and uniform vs. normal distributions using NumPy/Pandas
Hands‑on: loading CSVs, basic plotting with Matplotlib

2. Data Visualization & Simple Linear Regression

Crafting clear scatterplots: labels, grids, styling
Single‑variable linear regression (attendance → concessions)
Train‑test splitting and dealing with outliers
Evaluating models with R²; interpreting residuals
Extended example: car‑sales dataset, predicting price from one feature

3. Binary Classification & Logistic Regression

From regression to classification: why logistic vs. linear
Implementing logistic regression on an employee “stay/leave” dataset
Classification metrics deep dive: accuracy, precision, recall, F1 score, ROC curve
Understanding variability: train‑test ratios, data shuffling, sample size effects
Confusion matrix analysis

4. k‑Nearest Neighbors & the Iris Dataset

Introduction to k‑NN: distance metrics, choosing k
Dataset exploration: sepal/petal measurements, plotting clusters
Preprocessing: label encoding categorical data, feature scaling
Model training, hyperparameter tuning, evaluating with confusion matrix and classification report
Brief intro to decision‑tree logic (setting up for ensembles)

5. Ensemble Methods & Neural Networks

Random forest classifiers on the Titanic dataset: feature engineering, importance scores
Kaggle workflow: generating predictions, submitting to competition
Neural network primer: perceptron to multilayer architectures
Hands‑on MNIST digit classification with Keras/TensorFlow in Colab

Python Machine Learning Bootcamp Virtual Classroom Live September 15, 2025

Price: $1,895

Enroll today to reserve your spot!

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