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Round your Machine Learning skills with IBM Introduction to Machine Learning specialization

  • 1.  Round your Machine Learning skills with IBM Introduction to Machine Learning specialization

    Posted 14 days ago
    Edited by Miguel Maldonado 13 days ago
    IBM just announced the Introduction to Machine Learning specialization on Coursera. As the lead curriculum developer for this set of  courses, I would like to share with you some of the highlights and how I think you can benefit the most from signing up today.
    The IBM Introduction to Machine Learning specialization will help you realize the potential of machine learning in a business setting. You will be able to realize the potential of machine learning and artificial intelligence in different business scenarios. You will also be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. Finally, you will learn how to evaluate your machine learning models and to incorporate best practices.
    The specialization has 4 courses:
    Exploratory Data Analysis for Machine Learning
    This course helps you make the most out of your data by using Exploratory Data Analysis, Feature Engineering, and Feature Transformation. You start with a quick dive into Modern AI and Machine Learning applications. Then you learn tools and techniques that will be very useful throughout the rest of the specialization. Data is the main asset for Machine Learning and AI. You will lean how to get your data in great shape, find insights, and test hypothesis.

    Supervised Learning: Regression
    This course starts your journey in Supervised Learning with Linear Regression, a powerful algorithm from Statistical Learning that will be a solid foundation for more modern algorithms. It also introduces several key concepts like error metrics, data partition, and regularization, which will be important for you to compare the results across different Machine Learning models and select the model that best suits your analysis. These concepts also help you make sure your Machine Learning model generalizes well with new data.

    Supervised Learning: Classification
    This course focuses on techniques that help you either explain past events or predict future events. Professionals like you use these techniques to, for example, predict whether a customer will return to a store, or the likelihood of a student to graduate from a specific course; or to explain the main drivers of past customer engagement, successful online marketing campaigns, and customer retention. Specific Supervised Learning classification techniques that you will learn include Logistic Regression, K-nearest neighbors, Decision Trees, and Ensemble Modeling. You will also learn to deal with data sets with unbalanced classes, in case you are trying to predict rare events.

    Unsupervised Learning
    This course walks you through techniques that help you leverage data that does not have a labeled outcome or event, or even to take a deeper dive into your data after you have performed Supervised Learning analysis on your data. Companies from around the globe use unsupervised learning to quantify the quality of their data, segment their customers, and group similar observations together for further analysis. This course walks you through clustering algorithms like K-Means, Hierarchical Clustering, and DBSCAN, as well as dimension reduction techniques like Principal Component Analysis and Matrix Factorization.
    Each course of the specialization has lectures with theoretical framework, code-along demonstrations, and a project that helps you highlight your analytical and Machine Learning skills. On top of submitting your final deliverables for peer review, you are highly encouraged to post them here on the online communities or on an online portfolio.

    If you are looking for a short, comprehensive specialization to brush up on the theory behind Machine Learning, gain hands-on practice in the most common open source frameworks for Machine Learning, and use this opportunity to highlight your Machine Learning skills, sign up today for the IBM Introduction to Machine Learning specialization.

    Miguel Maldonado

    IBM - Data and AI Learning

  • 2.  RE: Round your Machine Learning skills with IBM Introduction to Machine Learning specialization

    Posted 6 days ago
    Hi Miguel, Thank you for sharing. May I know if there is any significant difference between this course and IBM data science professional courses? Since machine learning with python is included in this course and seems all algorithms has been covered. It is appreciated if you explain the differences. Thanks

    Samira Gholizadeh