Complete Generative AI Course With Langchain and Huggingface

Explore Python control flow and conditional statements using if, elif, and else, including nested conditionals and practical examples like leap year checks, a calculator, and age-based pricing.

Master Python loops in this lecture, from for and while loops to range usage, nested loops, and loop control statements such as break, continue, and pass, with practical examples.

Explore core Python lists and list comprehension, covering creation, indexing, slicing, and common methods like append, insert, remove, pop, count, sort, reverse, and clear, plus nested and conditional comprehensions.

Explore Python tuples, an immutable, ordered collection. Learn how to create, access, slice, concatenate, and pack/unpack tuples, use count and index, and work with nested tuples and star unpacking.

Explore Python dictionaries: create, access, modify, and iterate; learn nested dictionaries, dictionary comprehension, and common methods with practical coding examples and frequency counting.

Explore real world uses of Python lists, from to-do tracking and inventory management to grade analysis and user feedback, with practical examples of append, remove, and in checks.

Explore how to define and call functions in Python, including parameters, default values, and return statements. Utilize positional and keyword arguments for flexible, readable code.

Learn practical Python function programming with conversions, validation, and data tasks, including temperature conversion, password strength, shopping cart totals, palindrome checks, recursion, file word frequency, and email validation.

Explore how lambda functions in Python create anonymous one-expression functions with the syntax lambda args: expression, and see practical examples like addition, even checks, and map usage.

Explore the map function in Python, applying a function to every item in an iterable and returning a map object, an iterator, with examples using square, lambda, and multiple iterables.

Learn how the Python filter function creates an iterator from an iterable by applying a condition, using lambdas, even numbers, greater than five, and dictionary data.

Learn to import Python modules and packages, install and manage libraries with pip, alias packages like numpy, and build custom modules and subpackages with __init__.py.

Explore Python's standard libraries and practical usage, from array and math to random, OS file operations, JSON serialization, CSV handling, date time and time utilities, and regex.

Master Python file operations for text and binary files, including creating, reading, writing, appending, and seeking using with open, read, write, rb, wb modes.

Create directories and manage files using the OS module, list items, join paths, and check existence with isfile and isdir, while distinguishing absolute and relative paths.

Master Python exception handling using try, except, else, and finally to handle errors gracefully without stopping programs. Explore examples like name errors, zero division errors, and file not found.

Learn how Python uses classes and objects to model real world entities, define a blueprint with __init__, initialize attributes with self, and build examples like bank accounts.

Explore Python inheritance through single and multiple inheritance with practical examples. Build classes like car and Tesla using super to initialize, and model a dog with animal and pet.

Explore polymorphism in python and object oriented programming through method overriding and abstract base classes, with dog, cat, and shape examples illustrating multiple forms.

Explore encapsulation in Python and its relation to abstraction, using getter and setter methods to manage public, protected, and private variables in classes.

Explore abstraction in oops by hiding complex implementation and exposing only essential features. Learn to implement abstract base classes and abstract methods in Python, using vehicle and car examples.

Learn Python magic methods (dunder methods) that define object behavior for built-in operations. Override __init__, __str__, and __repr__ to customize class representations and prepare for operator overloading.

Explore operator overloading in Python by implementing custom vector operations with magic methods like __add__, __sub__, __mul__, __truediv__, and __eq__, and learn to represent objects using __repr__.

Explore Streamlit, an open source app framework for machine learning and data science, building web apps with Python scripts and interactive widgets.

Demonstrate building an end-to-end machine learning web app with Streamlit by using a random forest classifier on the iris dataset, offering interactive sliders to predict species in real time.

Explore the NLP roadmap from text preprocessing to vectorization and modern models, covering tokenization, word embeddings, RNNs, transformers, BERT, and tools like NLTK, spaCy, TensorFlow, and PyTorch.

Explore practical NLP use cases that power everyday tasks like spell correction, automated email drafting, translation, and text-to-image/video, using LangChain and Hugging Face models.

Learn core NLP concepts like corpus, documents, vocabulary, and tokens, and master tokenization that converts paragraphs into sentences or words for text preprocessing in the complete generative AI course.

Delivers practical tokenization in NLP using NLTK and Spacey, converting paragraphs into sentences and words, and exploring sentence tokenize, word tokenize, and treebank word tokenizer while comparing NLTK and Spacey.

Explore text preprocessing with NLTK stemming techniques, including Porter stemmer, Snowball stemmer, and regex stemmer, their benefits, drawbacks, and impact on NLP tasks like sentiment analysis and vectorization.

Learn lemmatization with WordNet lemmatizer, compare it to stemming, and apply post tags and the WordNet corpus to produce meaningful root forms for q and a chatbots and text summarization.

Explore stopwords in natural language processing, remove them with NLTK, and compare stemming and lemmatization to clean text for vectorization and model training.

Apply parts-of-speech tagging in NLP with NLTK to assign POS tags to words in sentences, illustrated by 'Taj Mahal is a beautiful monument' and tags like PRP and JJ.

Learn named entity recognition with NLTK, tagging person, location, date/time, and money in sentences, and visualize entities with a chunker and entity graphs.

Outline an nlp sentiment analysis workflow from data to vectors, detailing tokenization, lowercasing, and regex cleaning; compare one hot encoded, BoW, tf-idf, Word2Vec, and train ML models.

Demonstrates how one hot encoding converts text into vectors using a vocabulary, with word-level examples and the upcoming contrast with bag of words and tf-idf.

Explore the advantages and disadvantages of one-hot encoding in NLP, including easy Python implementation and the risks of sparse matrices, overfitting, fixed-size input, lack of semantic meaning, and out-of-vocabulary issues.

See how bag of words converts text to vectors by lowercasing, removing stop words, and building a vocabulary with word frequencies for sentiment classification tasks.

Analyze bag of words, a simple, intuitive method with fixed-size inputs, and compare its advantages to one-hot encoding with its disadvantages like sparse matrices and out-of-vocabulary issues.

Explore n-grams, from unigram to trigram, and see how they enrich bag of words and tf-idf representations by capturing context and distinguishing sentences.

Explore n-gram techniques using nltk to build bag-of-words features, adjust ngram_range (unigram, bigram, trigram) and max features, and observe vocabulary changes through fit_transform on a text corpus.

Explore tf-idf, a vectorization method that combines term frequency and inverse document frequency to convert sentences into vectors, compare it with bag-of-words, and prepare data for NLP classification.

Explore tf-idf's advantages over bag of words, including capturing word importance through term frequency and inverse document frequency. Learn its sparsity and out-of-vocabulary drawbacks and why it improves accuracy.

Implement tf-idf in python using sklearn's tf-idf vectorizer on a spam dataset, replacing stemming with wordnet lemmatization, preprocess with cleaning, lowercasing, stopwords, and explore n-grams and top features.

Explore how word embeddings convert words into vectors to capture meaning and similarity, and compare count-based methods with word2vec, cbow and skip gram.

Learn word2vec intuition, a neural network based word embedding that converts words into 300-d vectors, preserves meaning, and uses cosine similarity to reveal synonyms and opposites.

Learn how word2vec cbow and skip gram create word vectors from a corpus using a window and one hot encoding, trained via a neural network.

Explore the skip gram architecture for word two vec, contrasting it with cbow, and learn how window size, vocabulary, and training influence neural network inputs and outputs.

Word2vec replaces sparse bag-of-words representations with dense vectors, improving training and capturing semantic relationships. It enables finding similar words via cosine similarity and uses fixed dimensions to reduce out-of-vocabulary issues.

Learn how to convert words into 300-dimensional vectors with word2vec for text classification. Average these word vectors to produce a single 300-dimension sentence vector for model input.

Explore word2vec practical implementation with Google News 300 pre-trained vectors in gensim, and see how most_similar and king minus man plus woman yield queen.