NLP - Building your own chatbots using AI

Compare the two types of chat bots, rule based and AI-based self-learning, highlighting preprogrammed rules versus adaptive learning.

Explore rule-based chatbots that map keywords to intents and select predefined responses from two databases, enabling apps like ticket booking or food delivery with no intelligence involved.

Learn to implement a simple rule-based chatbot in Python by mapping intents to keyword lists and responses with dictionaries, normalizing input to lowercase, and selecting random replies.

Explore self-learning, NLP-based chatbots that adapt and learn from user interactions, overcoming rule-based limitations with dynamic, semantic and syntactic language understanding.

This lecture explains two chatbot operation modes—text based and voice based—and explores hybrid chatbots like Alexa or Google Assistant, highlighting speech recognition and text-to-speech as essential modules.

Learn the fundamentals of natural language processing, teaching computers to understand and generate human language using algorithms, with two core modules: understanding language data and generating responses.

Explore core natural language processing terms, including corpus (collections of sentences), documents (single sentences), terms and tokens, sentiments, and tokenization (converting sentences into meaningful words).

Discover Python's NLTK toolkit for natural language processing, including tokenisation, stemming, tagging, and semantic reasoning; learn to access WordNet via Python and install with a demonstration.

Explore nltk wordnet through the nltk corpus to access synonyms, definitions, examples, and part-of-speech data. Use these capabilities to generate chatbot responses and enhance natural language understanding.

Explore natural language processing applications such as language translation, text summarization, sentiment analysis, speech recognition, and part of speech recognition, including chatbots.

Learn to build an NLP chat bot with a language understanding and generation loop, from data collection and preprocessing to encoding text as vectors, computing cosine similarity, and generating responses.

Learn data preprocessing steps for NLP, including converting text to lowercase, tokenizing into sentences and words, removing noise and stop words, and encoding text for chatbot training.

Explore stemming and lemmatization to extract the root form of words. Learn five preprocessing steps—lowercase conversion, tokenization, noise removal, stoppers removal, and amortization—and apply them to a Python chatbot.

Encode text into numerical vectors in nlp using the tf-idf vectorizer by calculating term frequency and inverse document frequency, enabling chatbots to learn from preprocessing steps.

Compute tf-idf values for two documents to illustrate term frequency, inverse document frequency, and their product, forming document vectors whose size equals the corpus.

Learn how a chatbot uses tf-idf vectorization and cosine similarity to compare a user query against a static corpus and retrieve the most similar document.

Begin coding a python chatbot by preparing a corpus, importing libraries, and implementing tokenization to produce sentence and word tokens, then encode data and prepare for cosine similarity-based responses.

Define and implement lemmatization using wordnet, tokenize text, remove punctuation, and apply a lemmatizer to each token to produce normalized words.

Builds a rule-based greeting module for a chatbot by tokenizing and lowercasing user input, checking for greeting tokens, and returning a random greeting response from predefined lists.

Write a respond function that generates chatbot replies from corpus using tf-idf encoding, tokenization stop word removal, lemmatization, and cosine similarity to select sentence, and fallback reply if no match.

Build a practical chatbot by coding a main loop and preprocessing input to lowercase. Handle exit commands and generate responses from the greeting function or from the corpus.

Build a dynamic web surfing bot by integrating the Wikipedia API to fetch summaries based on user queries for a dynamic NLP chatbot corpus.

Explore ai-based chatbots, from neural networks and RNNs to transformers and large language models, and learn how pre-trained foundation models can be adapted to specific applications.

Explore artificial neural networks as function approximators that learn from data, infer patterns, and predict outcomes like house prices, enabling chatbots and intelligent decisions.

Explore how neural networks learn from data through neurons, weights, and interconnections, and how a fully connected network uses input, hidden, and output layers with activation functions to minimize error.

Explain why simple fully connected neural networks struggle with sequence data for chatbots, and trace the rise of recurrent neural networks, long short-term memory, transformers, and large language foundation models.

Discover how recurrent neural networks model sequential data with a feedback loop and a hidden state, linking current inputs to past outputs for chatbots and NLP tasks.

Master a basic rnn driven chatbot by building from scratch in TensorFlow and Keras, using embedding layers, character encoding, pad sequences, and transfer learning insights.