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.
- 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
Part 1: Machine Learning In Python [7 courses]
Introduction to Supervised Machine Learning in Python 9hObjectives
- 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 4hObjectives
- 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 3hObjectives
- 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 8hObjectives
- 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 2hObjectives
- 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 10hObjectives
- 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 12hObjectives
- 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
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.
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
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
Challenge yourself with exercises
Work with real data from day one with interactive lessons and hands-on exercises.
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
Build a K Nearest Neighbors classifier to predict whether patients might be at risk of heart disease.
Guided Project: Credit Card Customer Segmentation
For this project, we’ll build a clustering model to segment credit card customers into different groups in order to apply different solutions for each type of customer.
Guided Project: Predicting Insurance Costs
In this guided project, practice linear regression modeling and evaluation.
Guided Project: Stochastic Gradient Descent on Linear Regression
In this project, you will load, explore, and prepare a dataset to build a stochastic gradient descent regression model (linear regression), and then you will measure the efficiency of the model and visualize the results.
Guided Project: Classifying Heart Disease
In this guided project, you will practice the machine learning workflow and practice creating and optimizing a logistic regression to detect heart disease.