FREE COURSE – IBM Machine Learning Professional Certificate


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Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics.
This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries and you will apply what you have learned in various courses by completing a final capstone project.
Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.

This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:Tools: Jupyter Notebooks and Watson StudioLibraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.

Prepare for a career in machine learning. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months.


What you’ll learn

  • Master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles

  • Learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python

  • Develop working knowledge of KNN, PCA, and non-negative matrix collaborative filtering

  • Predict course ratings by training a neural network and constructing regression and classification models

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