IBM Machine Learning Professional Certificate
Prepare for a career in the field of machine learning. In this program, you’ll learn in-demand skills like AI and Machine Learning to get job-ready in less than 3 months. Machine Learning is the use and development of computer systems that are able to learn and adapt by using algorithms and statistical models to analyze and draw inferences from patterns in data. Machine Learning is a branch of Artificial Intelligence (AI) where computers are taught to imitate human intelligence in that they solve complex tasks. Roles available to those proficient in Machine Learning include machine learning engineer, NLP scientist, and data engineer. This program consists of courses that provide you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. Topics covered include Supervised and Unsupervised learning, Regression, Classification, Clustering, Deep learning and Reinforcement 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. Upon completion, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support. Applied Learning Project This Professional Certificate has a strong emphasis on developing the real-world skills that help you advance a career in Machine Learning and Deep 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 to use on Machine Learning jobs. These skills include: Tools: Jupyter Notebooks and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow. Algorithms: Supervised and Unsupervised learning, Regression, Classification, Clustering, Linear Regression, Ridge Regression, Machine Learning (ML) Algorithms, Decision Tree, Ensemble Learning, Survival Analysis, K-means clustering, DBSCAN, Dimensionality Reduction
