Machine Learning | Deep Learning | Explainable AI | Vertex

Course Covers Basic & Advanced Contents relevant to practitioners

Ratings 4.35 / 5.00

Machine Learning | Deep Learning | Explainable AI | Vertex

What You Will Learn!

You will learn the core concepts in Machine learning and Deep Learning
How to code and access data stored in a cloud environment
You will learn the core algorithms in ML: Linear Regression, Logistic Regression, Decision Tree, Random Forest
You will also learn about unsupervised learning
What is Explainer AI and why its important
You will master deep learning concepts and algorithms
What is a tensor and how it is helpful in deep learning
What are the linear algebra concepts relevant to Machine Learning and Deep Learning
How to go about a ML project
Python programming (for those who don't know python)
What is AutoML and how to use Vertex AI to deploy Machine learning algorithms

Description

Course Description

Machine learning is a subset of artificial intelligence that is at the forefront of digital transformation in the world. Thanks to machine learning, it is now possible to detect diseases, know the defaulters of a loan and know the future sales of a product. All these information can be had proactively and not as an after the fact scenario. Machine learning and artificial intelligence-based roles are in great demand in the job market and such roles offer a higher salary than traditional programming roles.

This course covers the concepts of machine learning as well as the application of these concepts using case studies and examples, along with a walk through of the python codes. Python programming is also covered for the benefit of those who are new to python and those who want to refresh some of the topics in python.

The following algorithms are covered in detail:

Simple and multiple linear regression
Logistic regression
Decision tree, Random forest and XG boost
Unsupervised algorithms - Cluster (kNN based) and Hierarchical.

Learners will also understand how to develop the above machine learning in a cloud environment. They will learn not just to code in cloud but also to access the data stored in cloud. This will be particularly helpful to learners since many organizations are adopting cloud at a fast pace.

A key aspect of the course is the coverage of Exploratory Data Analysis (EDA). EDA covers the set of activities that you do before you start the ML project.

Lastly, how to pursue a machine learning project has been covered.

This course is taught by an industry veteran, who brings his vast experiences and practical perspectives into the program.