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 6h
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 4h
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 4h
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
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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.
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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
Share the evidence of your hard work with your network and potential employers.
Projects in this path
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.
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.
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.
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.
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
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