This course provides advanced-level training on Machine Learning applications and algorithms. It will give you hands-on experience in multiple, highly sought-after machine learning skills in both supervised and unsupervised learning. This machine learning training ensures you can apply machine learning algorithms like regression, clustering, classification, and recommendation. The unique case study approach ensures you are working hands-on with data while you learn. You’ll also receive training in deep learning and Spark Machine learning—skills which are in great demand today.
What are the course objectives?
After completing this course, you will be able to:
- Classify the types of learning including supervised and unsupervised
- Identify the various applications of machine learning algorithms
- Perform supervised learning techniques: linear and logistic regression
- Understand classification data and models
- Use unsupervised learning algorithms including deep learning, clustering, and recommendation systems
- Use machine learning with Spark
Who should take this course?
The work for this course will be performed in Python/R. We have an introduction to these languages as part of the course. This course is best suited for:
- Analytics professionals who want to work in machine learning or artificial intelligence
- Data Science professionals who already have experience in R or Python
- Professionals working in eCommerce, search, and other online consumer based organizations
- Software professionals looking for a career switch into the field of analytics
- Graduates looking to build a career in Data Science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insight about customers
Prerequisites: This course will be performed in python / R hence we have an introduction to these languages in the program itself.
What projects will I complete as part of the course?
Project 1: Movie Recommendations
For this project, you’ll receive provide data about movies and users which will be used to train the model and generate recommendations for users about which movies they would like to watch, using the collaborative filtering technique.Project 2: Predict Loan Defaults
The data set for this project contains information on customers who received a Home Equity Line of Credit. The target variable is a flag. If the value is a “1” then the person defaulted on the loan. If the value is a “0” then the person repaid the loan. You’ll use the training data to develop a model to predict whether a customer will default on a loan, using the logistic regression technique.Submit one of the projects and on successful completion participant will receive an experience certificate.
Lesson 1: Introduction to Machine Learning
Lesson 2: Walking with Python or R
Lesson 3: Machine Learning Techniques
Lesson 4: Supervised Learning
Lesson 5: Supervised Learning – Regression
Lesson 6: Supervised Learning – Classification
Lesson 7: Unsupervised Learning
Lesson 8: Unsupervised Learning – Clustering
Lesson 9: Unsupervised Learning – Recommendation
Lesson 10: Unsupervised Learning – Deep Learning
Lesson 11: Spark Core and MLLib