## 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

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.