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The Machine Learning and Artificial Intelligence Bundle normally costs $480 and you can get it for just $39 in our deals store.
Comprehensive Learning Experience
Over this course, you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. NLP is a crucial area of AI that enables machines to understand, interpret, and respond to human language in a valuable way. You’ll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. These projects are not just academic exercises; they are practical tools that can be applied in real-world scenarios, such as filtering out unwanted emails or gauging public sentiment on social media.
Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning. By understanding the underlying principles, you can better appreciate how AI systems are designed and implemented. This knowledge is invaluable whether you’re looking to advance your career, start a new project, or simply satisfy your curiosity about how these technologies work.
Course Modules and Their Importance
Here is what is included in the bundle:
- Easy Natural Language Processing (NLP) in Python
- Unsupervised Machine Learning Hidden Markov Models in Python
- Cluster Analysis and Unsupervised Machine Learning in Python
- Data Science: Supervised Machine Learning in Python
Each module in this bundle is designed to build on the previous one, ensuring a comprehensive understanding of machine learning and AI.
1. Easy Natural Language Processing (NLP) in Python: This module introduces you to the basics of NLP, including text preprocessing, tokenization, and sentiment analysis. You’ll learn how to use Python libraries like NLTK and spaCy to manipulate and analyze text data.
2. Unsupervised Machine Learning Hidden Markov Models in Python: Hidden Markov Models (HMMs) are a powerful tool for modeling time series data and sequences. This module will teach you how to implement HMMs in Python, providing a foundation for more advanced topics like speech recognition and bioinformatics.
3. Cluster Analysis and Unsupervised Machine Learning in Python: Clustering is a type of unsupervised learning that groups data points into clusters based on their similarities. This module covers various clustering algorithms, including K-means and hierarchical clustering, and shows you how to apply them to real-world datasets.
4. Data Science: Supervised Machine Learning in Python: Supervised learning involves training a model on labeled data to make predictions. This module covers essential algorithms like linear regression, decision trees, and support vector machines. You’ll learn how to evaluate model performance and fine-tune your models for better accuracy.
By the end of this bundle, you’ll have a solid understanding of both supervised and unsupervised machine learning techniques, as well as practical experience in applying these techniques to solve real-world problems.
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