Overview

The Machine Learning and NLP Basics course is a learning resource designed for individuals interested in developing foundational knowledge of machine learning (ML) and natural language processing (NLP). This course is ideal for students, data scientists, software engineers, and anyone seeking to build or strengthen their skills in machine learning and natural language processing. Whether you are starting your journey or seeking to reinforce your foundation, this course provides practical skills and real-world applications. Throughout this course, participants will gain a solid understanding of machine learning fundamentals, explore various ML types, work with classification and regression techniques, and engage in practical assessments. 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. Learners are expected to have a basic understanding of programming. Familiarity with Python and AI fundamentals is helpful but not required. It is designed to equip learners with the skills and confidence necessary to navigate the evolving landscape of AI and data science, laying a strong foundation for further learning and professional growth.

Syllabus

Machine Learning

This module introduces Machine Learning (ML) fundamentals, types, and applications. It covers supervised, unsupervised, semi-supervised, and reinforcement learning, along with key methods like classification, regression, decision trees, random forests, and optimization.

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.