Central hub and bookmark for the 17-week training course on Machine Learning with a focus on Natural Language Processing.
Welcome to the central hub for this 17-week training course focused on Machine Learning with a specialization in Natural Language Processing (NLP). This page serves as a comprehensive guide and a central bookmark for the student to access materials for each week and the final project.
This course is structured into 17 weekly modules, each with a dedicated repository containing relevant materials and exercises. The course concludes with a final project repository.
For week 1, the student will be introduced to the fundamental concepts of Machine Learning as a subfield of artificial intelligence that enables computers to learn from data without explicit programming. The focus will then narrow to Natural Language Processing (NLP), a branch of AI concerned with the interaction between computers and human language, empowering computers to understand, interpret, and generate human language.
This introductory module will establish the foundational understanding necessary for the subsequent weeks of the course.
Repository: tec-stay-week-1
Week 2 of the course focuses on essential techniques in Natural Language Processing (NLP): Text Preprocessing and Tokenization. The student will learn how to prepare raw textual data for analysis by implementing fundamental preprocessing steps, including lowercasing and punctuation removal.
Additionally, the student will explore the concept of tokenization, which is the process of breaking down text into individual units known as tokens for subsequent analysis. A practical exercise will provide hands-on experience in implementing a simple tokenizer.
Repository: tec-stay-week-2
In Week 3, the student will delve into two fundamental concepts in Machine Learning (ML) and Natural Language Processing (NLP): Language Modeling and N-grams. Language modeling involves predicting the next word in a sequence based on preceding words, with modern models like GPT and BERT learning contextual structures.
The student will also learn about N-grams, which represent text as sequences of n-length words. These can be used as features in various NLP tasks, such as sentiment analysis. Understanding these concepts is crucial for enabling machines to understand and generate human-like text.
Repository: tec-stay-week-3
Week 4 introduces the student to Maximum Likelihood Estimation (MLE), a statistical method for estimating the parameters of a statistical model. The core principle involves finding the set of parameters that maximizes the probability of observing the given data under the assumed model.
The student will explore this concept through examples, such as coin flipping, to understand how MLE provides a principled method for finding the “best” parameters. This technique is widely applied in various fields, including the training of machine learning models.
Repository: tec-stay-week-4
Week 5 covers the basics of Neural Networks, a type of machine learning model inspired by the human brain, consisting of interconnected layers of nodes or “neurons.” These networks are powerful tools for recognizing complex patterns and making decisions.
The student will learn how neurons process information through weighted sums and activation functions, and how the network learns optimal weights during training via processes like gradient descent. Training involves iteratively adjusting weights to minimize the discrepancy between predicted and actual outputs.
Repository: tec-stay-week-5
In Week 6, the student will study Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which are advanced variants of recurrent neural networks (RNNs). LSTMs are specifically designed to address the long-term dependency problem in standard RNNs through the use of ‘memory cells’ and ‘gates’ (forget, input, output).
GRUs are a simpler variant, merging the memory cell and hidden state and using fewer gates (reset, update). Both LSTMs and GRUs are particularly effective for sequence prediction problems, widely used in tasks like language modeling and machine translation, enabling models to maintain context over long sequences.
Repository: tec-stay-week-6
Week 7 introduces the student to Word Embeddings, a critical breakthrough in Natural Language Processing (NLP). These are word representations that map words or phrases into vectors of real numbers, capturing semantic and syntactic meaning and relationships.
The student will learn how these vectors are learned using neural networks, often through models like Word2Vec and GloVe, where words sharing common contexts are mapped to nearby points in the vector space. Word embeddings are widely used as dense representations in numerous NLP tasks, including text classification, similarity, and sentiment analysis.
Repository: tec-stay-week-7
In Week 8, the student will explore the Encoder-Decoder architecture, a common model used in sequence-to-sequence tasks in Natural Language Processing (NLP), such as machine translation and text summarization. This architecture consists of two main components: an encoder and a decoder, often implemented using RNNs, LSTMs, or GRUs.
The encoder processes the input sequence into a context vector, and the decoder generates the output sequence based on this vector. The student will understand how this architecture works and its applications, as well as its limitations, particularly with long sequences, which led to the development of attention mechanisms.
Repository: tec-stay-week-8
Week 9 focuses on the Attention mechanism, a significant enhancement to traditional sequence-to-sequence models in NLP tasks. Attention allows the model to focus on specific parts of the input sequence when generating the output, rather than relying solely on a fixed-size context vector.
The student will learn how attention computes a weighted sum of the encoder’s outputs, where weights (attention scores) indicate the relevance of each input element to the current output step. This mechanism improves handling of long sequences, enhances interpretability, and has led to state-of-the-art results, notably forming the basis of Transformer models.
Repository: tec-stay-week-9
In Week 10, the student will learn about applying Supervised Learning to Sentiment Analysis. Sentiment analysis, or opinion mining, involves determining the emotional tone (positive, negative, neutral) of text. Supervised learning, which learns from labeled data, is a common approach for this task.
The student will understand the typical steps involved: data collection, preprocessing, model training using algorithms like logistic regression, SVM, or neural networks, and evaluation. Despite challenges like sarcasm and context, supervised learning, combined with advances like deep learning and word embeddings, provides an effective method for training sentiment analysis models.
Repository: tec-stay-week-10
Week 11 covers Transfer Learning, a technique where a model pre-trained on one task is adapted for a different but related task. This allows the student to leverage knowledge gained from large datasets, especially beneficial when labeled data for the specific task, such as emotion detection, is limited.
Emotion detection aims to classify text into multiple emotion categories (joy, sadness, anger, etc.), a more complex task than simple sentiment analysis. The student will see how pre-trained NLP models like BERT can be fine-tuned on emotion detection datasets, utilizing their learned understanding of language structure and semantics to improve performance.
Repository: tec-stay-week-11
In Week 12, the student will explore how Deep Learning models are leveraged for Sentiment Analysis. Unlike traditional methods, deep learning models, based on multi-layered neural networks, can learn high-level features and capture complex relationships and temporal dependencies in text.
The student will learn about the application of models like Recurrent Neural Networks (RNNs), specifically LSTMs, Convolutional Neural Networks (CNNs), and Transformers (like BERT) for sentiment analysis. These models have shown superior performance by better understanding context and nuances, though they require substantial data and computational resources, partially mitigated by transfer learning.
Repository: tec-stay-week-12
Week 13 focuses specifically on using Pre-Trained Transformer Models, specifically BERT, for Emotion Detection. BERT, pre-trained on large text corpora, excels at capturing syntax, semantics, and context bidirectionally.
The student will learn the process of fine-tuning a pre-trained BERT model on an emotion detection dataset. This approach allows the model to leverage its general language understanding while adapting to the specific task of recognizing emotions. While powerful due to its contextual understanding, the student should be aware of the computational resources required.
Repository: tec-stay-week-13
In Week 14, the student will explore the application of sentiment analysis and emotion understanding in the context of Chatbots. Chatbots are AI software designed for natural language interaction, used across various industries like customer service, e-commerce, and healthcare.
The student will learn how enabling chatbots to understand user emotions significantly enhances interaction quality and user experience, allowing for empathetic and contextually appropriate responses. Techniques discussed include basic sentiment analysis, more sophisticated emotion detection, and leveraging pre-trained language models and transfer learning for this purpose.
Repository: tec-stay-week-14
Week 15 introduces the student to Sequence Labeling, specifically for Named Entity Recognition (NER), using Conditional Random Fields (CRFs). NER is an NLP task that identifies named entities (persons, organizations, locations) in text by assigning a label to each token in a sequence.
CRFs are statistical models well-suited for this, as they consider the context within the input sequence and dependencies between labels when making predictions. The student will learn how CRFs work in NER, leveraging features of nearby tokens and previous predictions, and their benefits for contextual understanding and boundary detection.
Repository: tec-stay-week-15
In Week 16, the student will study Dependency Parsing, an NLP task that analyzes the grammatical structure of a sentence by identifying dependencies between words, forming a dependency tree or graph.
The student will learn about different Dependency Parsing Algorithms, including Transition-Based Parsing (Shift-Reduce Parsing) and Graph-Based Parsing (like Eisner’s Algorithm). The module will also introduce key Libraries for Dependency Parsing, such as SpaCy, Stanford NLP, NLTK, and SyntaxNet, providing tools for implementing these techniques.
Repository: tec-stay-week-16
Week 17 covers Text Summarization, an NLP subfield focused on creating concise summaries of longer texts while retaining key information. The student will learn about the two main approaches: Extractive Summarization, which extracts important sections directly from the source text, and Abstractive Summarization, which generates new sentences to paraphrase and condense information.
The module will compare these approaches, discussing their use cases, challenges, and the algorithms/techniques employed, including rule-based methods, machine learning, and deep learning models like sequence-to-sequence architectures and Transformers.
Repository: tec-stay-week-17
The final project for this research stay involves tackling the pivotal challenge of accurately detecting and classifying emotions from textual data. The student will gain hands-on experience in preparing, analyzing, and annotating a text dataset for emotion, applying three distinct computational approaches: rule-based processing, traditional neural networks, and deep learning methods.
The core objective is for the student to implement the provided Python program capable of processing the dataset and training models for each approach. The final deliverable is a formal written report that critically analyzes the performance differences, nuances, strengths, and limitations inherent in each method, providing valuable insights into their respective efficiencies and applicability in various emotion detection scenarios.
Repository: tec-stay-final
This concludes the outline of the course modules and final project. The student is encouraged to explore each repository for detailed materials and exercises.