What you'll learn
- Machine learning concepts
- How to code and access data stored in a cloud environment
- Simple and Multiple Linear Regression
- Logistics Regression
- Decision Tree
- Random Forest
- XG Boost
- Unsupervised Algorithms – Centroid (kNN based) and Hierarchical
- How to go about a ML project
- Python programming
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.
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.
Who this course is for:
- Professionals wanting to shift to ML roles
- ML professionals who are looking for a refresher