“Dataquest has perfected a teaching method that promotes self-learning. It has also created a community of learners that supports each other and values the exchange of ideas.”

Aleksey Korshuk

Machine Learning Engineer @Coframe

Path overview

In this path, you’ll gain a strong understanding of supervised and unsupervised machine learning algorithms.

You’ll also learn some of the most important and used algorithms and techniques to build, customize, train, test and optimize your predictive models such as linear regression modeling, gradient descent, logistic regression modeling and decision tree and random forest modeling. Finally, you’ll learn optimization techniques that will help you to improve efficiency and accuracy.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll apply your skills to several guided projects with realistic business scenarios to build your portfolio and prepare for your next interview.

Key skills

  • Understanding the core mathematical concepts behind machine learning
  • Identifying applications of supervised and unsupervised machine learning models
  • Using algorithms such as linear regression, logistic regression and gradient descent
  • Applying optimization methods to improve your models

Path outline

Part 1: Machine Learning In Python [7 courses]

Introduction to Supervised Machine Learning in Python 8h

Objectives
  • Establish a machine learning workflow
  • Implement the K-Nearest Neighbors algorithm for a classification task from scratch using Pandas
  • Implement the K-Nearest Neighbors algorithm using scikit-learn
  • Evaluate a machine learning model
  • Find optimal hyperparameter values using grid search

Introduction to Unsupervised Machine Learning in Python 5h

Objectives
  • Identify applications of unsupervised machine learning
  • Implement a basic k-means algorithm
  • Evaluate and optimize the performance of a k-means model
  • Visualize the model
  • Build a k-means model using scikit-learn

Linear Regression Modeling in Python 4h

Objectives
  • Describe a linear regression model
  • Construct a linear regression model and evaluate it based on the data
  • Interpret the results of a linear regression model
  • Use a linear regression model for inference and prediction

Gradient Descent Modeling in Python 3h

Objectives
  • Code a basic Gradient Descent algorithm
  • Recognize the limitations of basic Gradient Descent
  • Contrast the basic Batch and Stochastic Gradient Descent uses
  • Visualize Stochastic Gradient Descent using Matplotlib
  • Apply Stochastic Gradient Descent in Python using Scikit Learn

Logistic Regression Modeling in Python 3h

Objectives
  • Describe a logistic regression model
  • Construct a logistic regression model and evaluate it based on the data
  • Interpret the results of a logistic regression model
  • Use a logistic regression model for inference and prediction

Decision Tree and Random Forest Modeling in Python 6h

Objectives
  • Create, customize, and visualize decision trees
  • Use and interpret decision trees on new data
  • Calculate optimal decision paths
  • Optimize trees by altering their parameters
  • Apply the random forest prediction technique

Optimizing Machine Learning Models in Python 4h

Objectives
  • Distinguish between different optimization techniques
  • Identify the best optimization approach for your project
  • Apply optimization methods to improve your model
  • Employ machine learning tools on various optimization methods

The Dataquest guarantee

Guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

Money

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

Projects in this path

Guided Project: Predicting Heart Disease

For this project, we’ll take on the role of a data scientist at a healthcare solutions company to build a model that predicts a patient’s risk of developing heart disease based on their medical data.

Guided Project: Credit Card Customer Segmentation

For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.

Guided Project: Predicting Insurance Costs

For this project, you’ll step into the role of a data analyst tasked with developing a model to predict patient medical insurance costs based on demographic and health data.

Guided Project: Stochastic Gradient Descent on Linear Regression

For this project, we’ll step into the role of data scientists aiming to predict the optimal time to go to the gym to avoid crowds. We’ll build a stochastic gradient descent linear regression model using Python.

Guided Project: Classifying Heart Disease

For this project, you’ll assume the role of a medical researcher aiming to develop a logistic regression model to predict heart disease in patients based on their clinical characteristics.

Plus 2 more projects

Build your project portfolio with the Data Analyst in Python path.

Learning resources

With the ever-growing applications of Artificial Intell...
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In 2022, there was an explosion of innovation in the fi...
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Want to boost your career prospects? Learn machine lear...
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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessi

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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