- Data Science
- Supervised Learning
- Text Mining
- Convolutional Neural Networks
- Tensorflow
- Data Preprocessing
- Deep Learning
- Applied Machine Learning
- Classification Algorithms
- Machine Learning
- Artificial Intelligence
- Recurrent Neural Networks (RNNs)
Machine Learning and NLP Basics
Completed by Jitam Bharadwaj
June 2, 2024
19 hours (approximately)
Jitam Bharadwaj's account is verified. Coursera certifies their successful completion of Machine Learning and NLP Basics
What you will learn
Understand machine learning basics, applying supervised, unsupervised, and reinforcement learning for predictive modeling.
Develop deep neural networks, using CNNs and RNNs with LSTM for image classification and sequence prediction tasks.
Use NLP techniques, such as tokenization, stemming, and text classification with bag-of-words and naive Bayes methods.
Engage in hands-on projects, applying ML and NLP concepts to real-world scenarios for practical experience.
Skills you will gain
