Chatbot using python (deep learning & NLP)

Explore the basics of Python, a readable and simple programming language, learn to write a rectangle area calculator, and see Python's versatility from website to data analysis and robot control.

Install Python and set up your development environment. Verify versions and pip, run code in the terminal, and explore user input, printing, data types, and type casting in Python.

Explore Python variables and core data structures—lists, tuples, and dictionaries—along with defining reusable functions to avoid repeating code.

Master control flow and loops in Python by using if-else decisions, typecasting, and for and while loops, with breakpoint debugging to understand how the code runs.

Learn how to use try, except, else, and finally to handle errors in Python code, including zero division errors, and adapt results when exceptions occur.

Learn how to organize python code with modules and libraries, import predefined functions, and keep main files clean by splitting code into reusable parts.

Learn how Python classes act as blueprints for objects, use an init method to set name and age, and see how get info and celebrate birthday promote readable, reusable code.

Deep learning, a subset of machine learning, uses neural networks with input, hidden, and output layers to learn from data and train models for computer vision and natural language processing.

Explain how neural networks imitate the brain with input, hidden, and output layers, trainable weights, and activation functions to enable learning, deep learning, and Python chatbot development.

Explore building a chatbot by defining a dataset with intent, pattern, and answer, and applying word embedding and a simple neural network to map inputs to intents and responses.

Create virtual environments in Python to run code with a specific Python version and manage dependencies per project. Activate and deactivate these environments to keep projects isolated.

Learn text preprocessing and word embeddings for chatbots using NLTK, tokenization, stemming, lemmatization, and bag-of-words vectors to prepare sentences for NLP models.

Learn to build a chatbot model with PyTorch by installing torch, setting up a virtual environment, and implementing a two-layer neural network with ReLU activation.

We connect model.py and util.py in train.py to build a chatbot training dataset using tokenization, bag-of-words, and x/y arrays for a PyTorch data loader.

Train a chatbot model on a training dataset in Python with PyTorch, using fc layers, cross-entropy loss, and an epoch-based training loop that saves the model state.

Learn to build a chat bot interface in python by importing a basic chat bot, instantiating the bot, handling user input, and iterating with a response loop.

Load and run a chatbot model to generate messages from user input using tokenization, intent mapping, and neural inference. Explore device selection, model loading, and high-confidence responses.

Set up and customize a Python chatbot (deep learning and nlp) from GitHub. Create a virtual environment, install dependencies, edit intent JSON, and train for REST API and button-based interactions.