Complete Generative AI for upskilling & learning initiatives

Explore Python basics, including syntax and semantics, case sensitivity, indentation, comments (single and multi-line), variable assignment, type inference, and common syntax errors with practical code examples.

Learn how to declare and assign variables in python, including naming conventions and common pitfalls. Grasp variable types, dynamic typing, type checking and conversion, with practical examples like a calculator.

Explain power law distribution and the 80/20 rule with Pareto distribution concepts, from sales to software and sports, and show how a relative change drives proportional changes.

Explore python operators by implementing arithmetic, comparison, and logical operations with practical examples, building a simple calculator that prints results for addition, subtraction, multiplication, division, flow division, modulus, and exponentiation.

Explore Python control flow through conditional statements, if, elif, and else, with practical examples, nested conditions, common errors, and real-world use cases for upskilling and learning initiatives.

Explore scalability in generative AI by using line graph to build scalable multi-agent workflows with fault tolerance, flexible agent logic, and enterprise-ready cloud tools.

Explore tuples in Python: create and access immutable ordered collections, pack and unpack values, slice and concatenate, and work with count, index, and nested tuples.

Explore real world uses of Python lists across to do lists, grades, inventory, and feedback, using append, remove, and membership checks to manage tasks and analyze data.

Learn how the map function in Python applies a function to every item in an iterable, returning a map object; explore using lambda, multiple iterables, and practical examples.

Learn to import and use Python modules and packages, from built-ins like math to external libraries like NumPy. Master import styles, aliases, and basic package creation to organize code.

Learn Python file operations for text and binary files: read entire content, line by line with newline handling, write and append, use read-write modes, and copy content between files.

Explore object oriented programming with classes and objects as blueprints, and learn to define attributes and methods with __init__ and self through a bank account example.

Explore Python inheritance through single and multiple inheritance, using parent and child classes, the super and __init__ methods, and real-world examples like car and Tesla.

Master polymorphism in Python through method overriding and abstract base classes, enabling objects from different classes to share a common interface and vary behavior.

Master operator overloading in Python by implementing magic methods to customize operations for a vector class, including addition, subtraction, multiplication, comparisons, and complex numbers.

Create an end-to-end ml app with Streamlet that loads the iris dataset, trains a random forest classifier, and predicts species using interactive sliders.

Learn how to build an NLP roadmap from Python text preprocessing to vectorization, covering tokenization, bag of words, tf-idf, word embeddings, RNN, LSTM, GRU, transformers, and spam classification.

Learn to balance false negatives and false positives in generative AI applications across domains—disease detection, market risk, and vaccination side effects—emphasizing true positives and true negatives to protect outcomes.

Learn to implement bag of words in language processing with NLTK, including stop words removal, stemming, and using count vectorizer with max features and binary options on a spam dataset.

Explore NLP in deep learning and sequential data, from ANN and CNN basics to RNN variants, encoders and decoders, self-attention, transformers, and generative AI with LLMs.

Apply z score to standardize features and compare scores across distributions, using mean and standard deviation, with Gaussian distributions and sklearn's standard scaler.

Explore step-by-step training of an artificial neural network, covering optimizers and loss functions to enhance generative ai upskilling initiatives.

Load a trained ANN model from pickle files, encode geography with one-hot encoding and gender with label encoding, scale inputs, and predict customer churn.

Integrate an artificial neural network with a Streamlit web app to deploy a customer churn predictor, loading the trained model, encoders, and scaler, and providing interactive inputs for real-time predictions.

Learn to determine the optimal hidden layers and neurons for an ANN using grid search, cross-validation, and heuristics, with a Keras classifier to identify the best model.

Develop an end-to-end sentiment analysis project using a simple RNN on the IMDB dataset, including data preprocessing, embedding layer, model training, and deployment with a Streamlit app.

Learn how embedding layers convert words into dense vectors for neural networks, replacing costly one-hot encoding, with word2vec guidance and 300-dimension feature representations for RNN inputs.

This lecture demonstrates a word embedding workflow in Keras TensorFlow, converting words to indices, padding sequences, and using an embedding layer to generate 10-dimensional vectors for a 10,000 word vocabulary.

Build an end-to-end Streamlit web app that loads a trained RNN model, preprocesses text, and predicts sentiment from IMDb reviews, with deployment to Streamlit cloud.

Explore the LSTM RNN architecture with forget gate, input and candidate memory, and output gate, plus the memory cell linking long-term and short-term memory via x_t and h_{t-1}.

Build an end-to-end streamlet web app that loads a trained LSTM model and tokenizer to predict the next word from a text sequence.

Learn how bidirectional RNN and bidirectional LSTM RNN harness forward and backward context to improve sequence predictions, including embedding, and coverage of one-to-many, many-to-one, and many-to-many tasks.