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 classification and regression techniques, and wrap up with practical assessments. Additionally, the course offers an in-depth look at deep learning concepts, TensorFlow usage, digit classification with neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. We'll also cover essential NLP topics, including text mining, text preprocessing, analyzing sentence structure, and text classification.
By the end of this course, you will be able to:
-Understand and apply core concepts of machine learning and NLP.
-Differentiate between various types of machine learning and when to use them.
-Implement classification, regression, and optimization techniques in ML.
-Utilize deep learning models for complex problem-solving.
-Navigate TensorFlow for building and training models.
-Explore CNNs and RNNs for image and sequence data processing.
-Explore NLP techniques for text analysis and classification.
This course caters to a wide audience, including students, budding data scientists, software engineers, and anyone with an interest in machine learning and natural language processing. Whether you're starting your journey in ML and NLP or looking to solidify your foundational knowledge, this course offers valuable insights and practical skills.
Learners are expected to have a basic understanding of programming concepts. Familiarity with Python and fundamental artificial intelligence concepts will be beneficial but is not mandatory.
The course is divided into four modules, each focusing on different aspects of machine learning, deep learning, and natural language processing. Each lesson includes video lectures, readings, practical assignments, and discussion prompts to foster interactive learning and application of concepts.
Embark on this educational journey to explore the fascinating world of machine learning and natural language processing. This course is designed to equip you with the knowledge and skills necessary to navigate the evolving landscape of AI and data science, setting a strong foundation for further exploration and innovation.
Overview
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 types—such 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.
Taught by
Edureka
