Description for ML and NLP Basics
Fundamentals of Machine Learning: Acquire a comprehensive comprehension of the fundamentals of machine learning, such as classification, regression, and ML techniques.
Methods of Deep Learning: Investigate the principles of deep learning, with a particular emphasis on the application of TensorFlow, digit classification, CNNs, RNNs, and LSTMs in the context of intricate data modeling.
Natural Language Processing (NLP): Learn critical NLP topics, including text mining, preprocessing, sentence structure analysis, and text classification, for practical applications.
Practical Evaluations: Take part in practical assessments to implement the techniques you have acquired and to enhance your comprehension of deep learning and machine learning models.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Edureka
Duration: 3 weeks at 6 hours a week
Schedule: Flexible
Pricing for ML and NLP Basics
Use Cases for ML and NLP Basics
FAQs for ML and NLP Basics
Reviews for ML and NLP Basics
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML and NLP Basics
Featured Tools
Become proficient in the utilization of Spring AI to facilitate the integration of sophisticated AI models into Java-based applications, with an emphasis on generative capabilities and prompt engineering.
Gain an extensive understanding of the strategies for optimizing chatbot applications, integration with NLP/ML, and advanced ChatGPT prompting.
Develop an understanding of the machine learning protocol, which encompasses the entire process from data preparation and model training to the dissemination of results to the organization.
With an emphasis on time series prediction using RNNs and ConvNets, this course educates software developers on how to create scalable AI models using TensorFlow.
Apply linear algebra concepts like linear independence, rank, singularity, eigenvalues, and eigenvectors to analyze data and solve machine learning problems using standard vector and matrix operations.