A short summary
This is the course for you if you want to pursue your career in Machine Learning.
This course is organized in such a way that you can understand all of the basics of machine learning as well as advanced concepts.
This course is divided into five parts, as follows:
Data & Introduction Machine learning wrangling
Machine learning: Linear Models, Trees, and Preprocessing
In machine learning, model evaluation, feature selection, and pipelines are all essential.
In machine learning, Bayes, Nearest Neighbors, and Clustering are used.
In machine learning, SVM, Anomalies, Imbalanced Classes, and Ensemble Methods are used.
In the tools area, you’ll find a GitHub connection for the code discussed in each lecture.
In this lesson, who is instructing you?
I’m a professional trainer and consultant for languages like C, C++, Python, Java, Scala, Big Data Technologies – PySpark, Spark using Scala, Machine Learning & Deep Learning – sci-kit-learn, TensorFlow, TFLearn, Keras, h2o, and have delivered at companies like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan – Singapore & I’ve expressed my experience in the hopes of assisting you in understanding the systemic solution to ML.
Right now, machine learning is taking place. So you’d like to get a head start on experimenting with ideas and innovations that enable it. As a result, when the time comes, your talents will be in even higher demand, and you will be able to ensure a career path that is still growing.
Enroll right now!! Look forward to seeing you in class.
Good luck with your studies.
Edyoda’s Team
This course is intended for the following individuals:
Beginners interested in pursuing a career as a computer scientist
Developers who want to practice machine learning from the ground up
Python programmers who like to practice machine learning
Professionals interested in pursuing a career in Machine Leaning
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