Machine Learning and NLP Basics

Edureka via Coursera

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Overview

Welcome to the "Machine Learning and NLP Basics" course, a comprehensive learning resource designed for enthusiasts keen on mastering the foundational aspects of machine learning (ML) and natural language processing (NLP). This course is structured to provide a deep dive into the core concepts, algorithms, and applications of ML and NLP, preparing you for advanced exploration and application in these fields. Throughout this course, participants will gain a solid understanding of machine learning fundamentals, dive into various ML types, explore...

Syllabus

Machine Learning

This module of our course offers a comprehensive dive into the fundamentals, types, and applications of Machine Learning (ML), a pivotal aspect of artificial intelligence. It is meticulously crafted to transition learners from the basics of AI and predictive models in ML to a deeper understanding of different ML typessuch as supervised, unsupervised, semi-supervised, and reinforcement learning. It further explores key concepts in classification and regression, including decision trees, random forests, and model optimization techniques. This module serves as both a foundational and an advanced exploration, catering to a broad spectrum of learners aiming to master machine learning.

Deep Learning

This module provides a comprehensive exploration of deep neural networks, covering fundamental concepts, practical implementations, and advanced techniques. From understanding the basics of deep learning and its comparison with human brain functioning to delving into specific architectures like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), this module equips learners with the knowledge and skills needed to design, train, and optimize deep learning models for various tasks, including image classification and sequence prediction

Natural Language Process

This Module introduces the fundamentals of text mining and analysis. It covers various techniques for extracting, cleaning, and preprocessing text data, including tokenization, stemming, lemmatization, and named entity recognition. Additionally, the module explores methods for analyzing sentence structure, such as syntax trees and chunking, along with text classification techniques using bag-of-words, count vectorizers, and multinomial naive Bayes classifiers. Through practical assignments and discussions, learners gain insights into the applications of text mining across different domains and the essential tools and processes involved in working with textual data.

Course Wrap-up and Assessments

This module is the final stage of the course, offering learners a comprehensive review and evaluation of the knowledge and skills acquired throughout the modules. Throughout the module learners engage in various activities to solidify their learning and assess their understanding of the course material. These activities include completing a practice project that applies learned concepts to real-world scenarios, undertaking a graded assignment to evaluate proficiency, and potentially viewing a course completion video summarizing key takeaways and achievements.