Description: Machine Learning Certification Training using Python helps you gain experienced professionalsise in various machine learning algorithms such as regression, clustering, decision trees, random forest, NaΓ―ve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR. Platform: Edureka Course Level: Newbie Duration: 2 Months Domain: Artificial Intelligence Paid/Free: Paid
Machine Learning with Mahout Certification Training
Description: An online course designed to provide a blend of Machine learning & Big Data and where Mahout fits in the Hadoop Ecosystem. Platform: Edureka Course Level: Newbie Duration: 2 Months Domain: Artificial Intelligence Paid/Free: Paid
Intro to Machine Learning with Tensorflow
Description: Learn foundational machine learning techniques – from data manipulation to unsupervised and supervised algorithms. Learning Outcome: Supervised Learning, Deep Learning, Unsupervised Learning Platform: Udacity Course Level: Newbie Duration: 3 Months Domain: Artificial Intelligence Paid/Free: Paid
Intro to Machine Learning with PyTorch
Description: Learn foundational machine learning techniques – from data manipulation to unsupervised and supervised algorithms. Learning Outcome: Supervised Learning, Deep Learning, Unsupervised Learning Platform: Udacity Course Level: Newbie Duration: 3 Months Domain: Artificial Intelligence Paid/Free: Paid
Machine Learning Engineer
Description: Learn advanced machine learning techniques and algorithms – including how to package and deploy your models to a production environment. Learning Outcome: Software Engineering Fundamentals, Machine Learning in Production, Machine Learning Case Studies, Machine Learning Capstone Platform: Udacity Course Level: Expert Duration: 3 Months Domain: Artificial Intelligence Paid/Free: Paid
Machine Learning for Business
Description: This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy. Learning Outcome: Machine learning and data use cases, Machine learning types, Business requirements and model design, Managing machine learning projects Platform: Datacamp Course Level: Newbie Duration: 4 hours Domain: Machine Learning Paid/Free: Paid
Machine Learning for Everyone
Description: In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone! Learning Outcome: What is Machine Learning?, Machine Learning Models, Deep Learning Platform: Datacamp Course Level: Newbie Duration: 4 hours Domain: Machine Learning Paid/Free: Paid
Practicing Machine Learning Interview Questions in R
Description: In this course, you will learn to answer 30 non-trivial questions that often pop up in ML interviews. These questions revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, selection, and evaluation. You will practice these concepts while learning to predict the rating of an Android app or segmenting mall customers based on their purchasing behaviors. Learning Outcome: Data preprocessing and visualization, Supervised learning, Unsupervised learning, Model selection and evaluation Platform: Datacamp Course Level: Newbie Duration: 4 hours Domain: Machine Learning Paid/Free: Paid
Machine Learning in the Tidyverse
Description: This course will teach you to leverage the tools in the “tidyverse” to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a “tidy” way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning. Learning Outcome: Foundations of “tidy” Machine learning, Multiple Models with broom, Build, Tune & Evaluate Regression Models, Build, Tune & Evaluate Classification Models Platform: Datacamp Course Level: Newbie Duration: 4 hours Domain: Machine Learning Paid/Free: Paid
Machine Learning for Marketing Analytics in R
Description: This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you’re going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You’re going to learn about and apply strategies to communicate your results and help them make a difference. Learning Outcome: Modeling Customer Lifetime Value with Linear Regression, Logistic Regression for Churn Prevention, Modeling Time to Reorder with Survival Analysis, Reducing Dimensionality with Principal Component Analysis Platform: Datacamp Course Level: Newbie Duration: 4 hours Domain: Machine Learning Paid/Free: Paid
