IBM Machine Learning Professional Certificate

The Machine Learning Professional Certificate course offered by IBM is a comprehensive program that covers various aspects of machine learning and artificial intelligence. The course aims to teach data scientists how to work with high-quality data by extracting and cleaning it, as well as applying feature engineering techniques. Students will also learn how to access data from different sources such as SQL databases, NoSQL databases, and APIs. The course covers topics like feature selection and engineering, handling missing values and categorical features, and dealing with outliers. Proficiency in Python programming and the Python development environment is essential for this course.

Overall, this course is designed for data scientists who are interested in machine learning and artificial intelligence in a business context. It provides the necessary skills and knowledge to effectively work with data and apply machine learning algorithms. By completing this course, students will be equipped with the expertise needed to excel in the field of artificial intelligence and data science.

This course will teach you about Regression, which is one of the most important types supervised machine-learning modelling families. This course will show you how to create regression models that predict continuous outcomes. For comparisons between models, you'll also be able to use error metrics. You will also learn best practices like regularization and train and test splits.

This course will teach you about the most important type of supervised machine-learning modelling families: classification. This course will show you how to create predictive models that can categorize outcomes. For comparisons between models, you'll also be able to use error metrics. This course covers practical aspects of classification. This course will teach you how to create and test split models, and how to handle data sets that contain unbalanced classes.

This course will teach you Unsupervised Learning, one of the most important types in Machine Learning. You will learn how to extract insights out of data that doesn't contain any labeled or target variables. Unsupervised learning will show you how to select the best algorithm for your data. This course covers unsupervised learning in its practical aspects.

This course will introduce you to Deep Learning and Reinforcement Learning, two of the most popular disciplines in Machine Learning. Deep Learning is a subset in Machine Learning. It is used in both Supervised as well as Unsupervised Learning and is often used to power many of the AI applications we use every day. You will first learn about Neural Networks and the modern architectures of Deep Learning. After you have created a few Deep Learning models the course will move on to Reinforcement Learning. This type of Machine Learning has been getting more attention lately. While Reinforcement learning has very few applications currently, it is an area of AI research that could be relevant in the future.

If you've completed the IBM Specialization courses in sequence, this course will give you a lot of practice and a solid knowledge of the main types and methods of Machine Learning. These include: Unsupervised Learning, Deep Learning and Supervised Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course is for data scientists who are interested in learning hands-on skills with Deep Learning and Reinforcement Learning. What skills are required? You should be familiar with Python programming and have a basic understanding of Data Cleaning, Exploratory Data Analysing, Unsupervised Learning, Supervised Learning.

This course will introduce you to Machine Learning topics that can be used in conjunction with essential tasks such as forecasting and analysing censored data. Learn how to analyze data with a time component, and how to infer the outcome from censored data. A few techniques will be taught for Survival Analysis and Time Series Analysis. This course focuses on applying best practices and validating assumptions derived through statistical learning.

This course will help you: Identify common modeling problems with time series data. Explain how to decompose Time Series Data: trend, seasonality and residuals. Describe survival modeling approaches and hazard modeling. Identify types suitable for survival analysis. This course is for data scientists who are interested in getting hands-on experience in Time Series Analysis and Survival Analysis. What skills are required? You should be familiar with Python programming to get the most from this course.