WHAT YOU WILL LEARN
Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction
Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn
Deploy machine learning algorithms and pipelines on Apache Spark
Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow
What you will learn in this specialization course
- Machine Learning with Python :This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
- Scalable Machine Learning on Big Data using Apache Spark : This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
- Introduction to Deep Learning & Neural Networks with Keras : This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
- Deep Neural Networks with PyTorch : The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch’s tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
- Building Deep Learning Models with TensorFlow : The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
- AI Capstone Project with Deep Learning : In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
After completing each course in specialization and complete the hands-on project, you’ll earn a Certificate.
Course Rating : 4.8
Instructors : Ibrahim Odeh, Ph.D., MBA
Offered By : Columbia University
Can I download IBM AI Engineering Professional Certificate course?You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The IBM AI Engineering Professional Certificate course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
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