Applied Machine Learning
- 0/100 Students
- 2 Lessons /0 Quizes
If you have an intermediate acquaintance of Python, and you are willing to expand your knowledge in Machine Learning, then this course from Columbia Engineering is an excellent choice for you. In this course, you will learn a wide variety of techniques of supervised and unsupervised machine learning approaches with Python programming language. The trail follows a practical approach that invites participants into a conversation, where you will learn with live subject matter experts. After completing this course, you will be equipped with a standard knowledge of Applied Machine Learning that can be implemented in various industries, such as Healthcare, Retailing, Software Development, etc.
Key USPs –
– Learn how to utilize the popular machine learning and deep learning libraries like SciPy, ScikitLearn, PyTorch, etc.
– Learn how TensorFlow is applied to industry problems involving text analytics, object recognition, natural language processing, and other types of classifiers
– Dive into the individual concepts of machine learning with an approachable and professional programming language, Python
– Obtain the skills to scale data science and machine learning tasks on Big Data sets using Apache Spark
– Be able to build, train, and deploy different types of deep architectures, as well as convolutional networks, recurrent networks, and autoencoders