9 Become a Machine Learning Specialist Courses by LinkedIn Learning – Perfect for BeginnersOctober 21, 2021 2021-10-21 9:48
9 Become a Machine Learning Specialist Courses by LinkedIn Learning – Perfect for Beginners
9 Become a Machine Learning Specialist Courses by LinkedIn Learning – Perfect for Beginners
LinkedIn Learning has compiled 9 Become a Machine Learning Specialist Courses with a free trial month included to help students learn Machine Learning while saving a buck.
This learning path shows how machine learning algorithms work and how to design them yourself. There’s a lot to learn in this rapidly growing (and highly recuited-for) field, and these courses will give you an extremely solid skill set.
The ‘Become a Machine Learning Specialist‘ learning path includes 9 different courses and provides a certificate of completion upon each course completion.
Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees.
Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
2. Deploying Scalable Machine Learning for Data Science – Click here to Enroll
Machine learning models often run in complex production environments that can adapt to the ebb and flow of big data. The tools and practices that help data scientists rapidly build machine learning models are not sufficient to deploy those models at scale. To deliver scalable solutions, you need a whole new toolset.
This course provides data scientists and DevOps engineers with an overview of common design patterns for scalable machine learning architectures, as well as tools for deploying and maintaining machine learning models in production. Instructor Dan Sullivan reviews three technologies that enable scalable machine learning: services that expose models through APIs, containers for deploying models, and orchestration tools like Kubernetes that help manage containers and clusters. Plus, get tips for monitoring the performance of your services in production environments.
3. Building a Recommendation System with Python Machine Learning & AI – Click here to Enroll
Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one.
She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you’re familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
4. Machine Learning and AI Foundations: Clustering and Association – Click here to Enroll
Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.
Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.
All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
5. Machine Learning & AI: Advanced Decision Trees – Click here to Enroll
If you’re working towards an understanding of machine learning, it’s important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms.
Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
6. Machine Learning and AI Foundations: Classification Modeling – Click here to Enroll
One type of problem absolutely dominates machine learning and artificial intelligence: classification. Binary classification, the predominant method, sorts data into one of two categories: purchase or not, fraud or not, ill or not, etc. Machine learning and AI-based solutions need accurate, well-chosen algorithms in order to perform classification correctly.
This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy (or strategies) for their projects. Instructor Keith McCormick draws on techniques from both traditional statistics and modern machine learning, revealing their strengths and weaknesses. Keith explains how to define your classification strategy, making it clear that the right choice is often a combination of approaches. Then, he demonstrates 11 different algorithms for building out your model, from discriminant analysis to logistic regression to artificial neural networks. Finally, learn how to overcome challenges such as dealing with missing data and performing data reduction.
Note: These tutorials are focused on the theory and practical application of binary classification algorithms. No software is required to follow along with the course.
7. Machine Learning and AI Foundations: Value Estimations – Click here to Enroll
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property’s location and characteristics.
In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
8. Machine Learning & AI Foundations: Linear Regression – Click here to Enroll
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.
Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
9. Machine Learning and AI Foundations: Recommendations – Click here to Enroll
This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations. In this course, Adam Geitgey walks you through a hands-on lab building a recommendation system that is able to suggest similar products to customers based on past products they have reviewed or purchased. The system can also identify which products are similar to each other.
Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you’ll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.