MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11 lectures (2 hour, 45 mins) | Size: 798 MB
Machine learning is a subset of artificial intelligence that is at the forefront of digital transformation in the world.
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
Requirements
Basic knowledge of Python
Description
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.
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
Students
ML professionals who are looking for a refresher
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