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]
- 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
- 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
- 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
- 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
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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.