Adrian Schmitt

Overview of Machine Learning in Python

Garbage in, garbage out. Your model is only as good as your data. Learn the essential data prep and evaluation workflow in Python.

Overview of Machine Learning in Python
#1about 2 minutes

Understanding the main paradigms of machine learning

Learn the distinctions between supervised, unsupervised, semi-supervised, and reinforcement learning based on data and goals.

#2about 1 minute

Differentiating between regression and classification tasks

Supervised learning is broken down into predicting continuous variables with regression and discrete labels with classification.

#3about 3 minutes

Why data preparation is a critical first step

The principle of 'garbage in, garbage out' highlights the need to analyze and clean data before training any model.

#4about 2 minutes

Converting categorical data into numerical features

Transform non-numerical string data into a machine-readable format using label, ordinal, and one-hot encoding techniques.

#5about 2 minutes

Standardizing numerical data with scaling techniques

Handle large or small numerical values that can negatively impact training by applying scaling methods like min-max or Z-score normalization.

#6about 2 minutes

Choosing a strategy for handling missing data

Address missing values in a dataset by either deleting the affected rows/columns or using imputation to replace them.

#7about 3 minutes

Analyzing a dataset for potential bias and imbalance

A practical example using the adult census dataset demonstrates how to identify and understand biases related to age, sex, and race.

#8about 5 minutes

Splitting data for model training and evaluation

Properly divide your dataset into training and testing sets using strategies like holdout, cross-validation, and stratification to avoid data leakage.

#9about 2 minutes

Using metrics to evaluate model performance

Measure classification model success with accuracy, precision, and F1 score, and regression model success with mean absolute or squared error.

#10about 3 minutes

Understanding overfitting and the bias-variance tradeoff

Find the optimal model complexity by balancing the training error and test error to avoid underfitting or overfitting.

#11about 3 minutes

Tuning hyperparameters and selecting the right algorithm

Optimize model performance by searching for the best parameters with grid search or randomized search, and explore meta-learning for algorithm selection.

#12about 3 minutes

Introduction to decision trees and random forests

Decision trees offer a transparent, white-box model, but random forests typically provide better performance by combining multiple trees.

#13about 7 minutes

Code demo: Preprocessing and training a classifier

A step-by-step Python example shows how to preprocess data, handle missing values, and train decision tree and random forest classifiers using scikit-learn.

#14about 4 minutes

Fundamentals of neural networks and perceptrons

Explore the basic building block of neural networks, the perceptron, and see how they are combined into multi-layer perceptron (MLP) architectures.

#15about 2 minutes

Overview of deep neural networks and architectures

Go beyond simple MLPs to understand deep neural networks and specialized architectures like CNNs for images and RNNs for language.

#16about 2 minutes

Common pitfalls and solutions for neural networks

Address common issues like overfitting with techniques such as dropout and regularization, and tackle the black box problem with model explainers.

#17about 2 minutes

Q&A: Scaling machine learning for large datasets

Handle large datasets by using parallelization to train smaller models simultaneously or by leveraging pre-trained components with transfer learning.

#18about 3 minutes

Q&A: Using scikit-learn for model evaluation

Effectively compare models in scikit-learn by using the built-in metrics module and visualizing results with tools like confusion matrices.

#19about 2 minutes

Q&A: Handling imbalanced datasets during training

Correct for imbalanced target labels in a classification problem by applying oversampling to duplicate instances of the minority class.

#20about 2 minutes

Q&A: Advantages of scikit-learn over other libraries

Scikit-learn is ideal for beginners due to its ease of use and wide variety of algorithms, though specialized libraries offer deeper customization.

#21about 2 minutes

Q&A: Identifying red flags in datasets

Watch for red flags like poor quality data, excessive missing values, and sensitive information that can compromise model performance and ethics.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Increasing the value of talk recordings post-event
04:57 MIN

Increasing the value of talk recordings post-event

Cat Herding with Lions and Tigers - Christian Heilmann

Organizing a developer conference for 15,000 attendees
01:32 MIN

Organizing a developer conference for 15,000 attendees

Cat Herding with Lions and Tigers - Christian Heilmann

Selecting strategic partners and essential event tools
03:17 MIN

Selecting strategic partners and essential event tools

Cat Herding with Lions and Tigers - Christian Heilmann

Why corporate AI adoption lags behind the hype
03:28 MIN

Why corporate AI adoption lags behind the hype

What 2025 Taught Us: A Year-End Special with Hung Lee

Automating formal processes risks losing informal human value
03:48 MIN

Automating formal processes risks losing informal human value

What 2025 Taught Us: A Year-End Special with Hung Lee

Why HR struggles with technology implementation and adoption
04:22 MIN

Why HR struggles with technology implementation and adoption

What 2025 Taught Us: A Year-End Special with Hung Lee

Moving beyond headcount to solve business problems
04:27 MIN

Moving beyond headcount to solve business problems

What 2025 Taught Us: A Year-End Special with Hung Lee

Balancing the trade-off between efficiency and resilience
03:38 MIN

Balancing the trade-off between efficiency and resilience

What 2025 Taught Us: A Year-End Special with Hung Lee

Featured Partners

Related Videos

See all videos
Machine learning 101: Where to begin?26:25

Machine learning 101: Where to begin?

Lutske De Leeuw

A beginner’s guide to modern natural language processing56:46

A beginner’s guide to modern natural language processing

Jodie Burchell

Getting Started with Machine Learning44:11

Getting Started with Machine Learning

Alexandra Waldherr

Multilingual NLP pipeline up and running from scratch52:37

Multilingual NLP pipeline up and running from scratch

Kateryna Hrytsaienko

Machine Learning for Software Developers (and Knitters)49:45

Machine Learning for Software Developers (and Knitters)

Kris Howard

The pitfalls of Deep Learning - When Neural Networks are not the solution32:54

The pitfalls of Deep Learning - When Neural Networks are not the solution

Adrian Spataru & Bohdan Andrusyak

PySpark - Combining Machine Learning & Big Data43:26

PySpark - Combining Machine Learning & Big Data

Ayon Roy

Introduction to Azure Machine Learning54:27

Introduction to Azure Machine Learning

Jose Luis Latorre Millas

Related Articles

View all articles
DC
Daniel Cranney
The State of WebDev AI 2025 Results: What Can We Learn?
Introduction The 2025 edition of The State of WebDev AI offers a detailed snapshot of how developers are using AI today, which tools have gained the most traction over the past year, and what these trends suggest about the future of the industry. In...
The State of WebDev AI 2025 Results: What Can We Learn?

From learning to earning

Jobs that call for the skills explored in this talk.