- Convolutional Neural Networks
- Recurrent Neural Networks (RNNs)
- Natural Language Processing
- Data Science
- Predictive Modeling
- Applied Machine Learning
- Machine Learning
- Data Preprocessing
- Text Mining
- Deep Learning
- Tensorflow
- Classification Algorithms
Machine Learning and NLP Basics
Completed by Palak Kale
January 3, 2025
19 hours (approximately)
Palak Kale'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
