Generative AI for Developer

Master Python basics as a high-level, interpreted language with readable syntax and dynamic typing. Explore libraries for data analysis and web development, and learn setup on Windows, macOS, and Linux.

Master python strings, including single-line and multi-line forms, and slice notation like 0:5 and -1, including newline usage, then declare name, age, and marks and print their data types.

Review Python list assignment answers by demonstrating append to add charger at end of products list, extend to include screen protector and phone case, and print updated list.

Explore Python tuples vs lists, including mutability, memory efficiency, and use cases for static data; learn tuple access and why append isn’t supported.

Explore Python file handling for machine learning workloads, including creating, writing, reading, and appending text files, and writing and reading CSV files with CSV writer and reader.

Explore supervised learning through labeled data, with regression and classification examples, from spam detection to image classification, and the role of training and testing data in improving model accuracy.

Unsupervised learning trains on unlabeled data to discover hidden patterns, using clustering to group similar customers and association to suggest product bundles or promos.

Learn how neural networks, inspired by the brain, power deep learning to recognize patterns in structured and unstructured data, including unsupervised learning with input, hidden, and output layers.

Explore how deep learning powers speech recognition, image recognition, and natural language processing in real-world apps, from voice search to photo tagging and text generation.

Explore the decision tree, a versatile, intuitive machine learning model that uses a tree-like graph for classification and regression, with applications in banking, e-commerce, and medical diagnosis.

Explore convolutional neural networks (CNNs) for analyzing visual imagery, automatically learning features and achieving translation invariance for robust image and video recognition, facial recognition, and medical imaging applications.

Explore recurrent neural networks (RNNs) that process sequential data and remember prior inputs. Apply them to language translation, sentiment analysis, speech recognition, and real-time translation using popular ML tools.

Learn how to evaluate machine learning models by identifying overfitting and underfitting, balancing training and testing accuracy, and using metrics and cross-validation to stop training at a right fit.

Apply supervised learning with TensorFlow to build an image classifier for cats and dogs, using 80% training and 20% validation, CNN layers, and RMSProp optimization with accuracy evaluation.

Save the trained model as a .h5 file, then load it with TensorFlow Keras and prepare 150 by 150 images by converting them to arrays with a batch dimension.

Load the trained supervised learning model in Colab, upload an image, preprocess to 150 by 150, normalize, and predict cat or dog with a 0.5 threshold, noting 15 training epochs.

Demonstrates creating a synthetic e-commerce data frame with Pandas and NumPy, saving to csv file, standardizing purchase amounts, and applying k-means clustering to reveal low, medium, and high spend groups.

Examine ethics, privacy, and legal aspects of AI, emphasizing fairness, accountability, transparency, and data protection. Analyze real-world risks like bias, privacy breaches, and regulatory frameworks such as GDPR and CcpA.

Define the scope and objectives for generative AI projects, set milestones, and monitor progress with dashboards; apply agile methods and cross-functional teams to deliver measurable improvements in operations and personalization.

Leverage GenAI to personalize product recommendations and drive targeted marketing. Analyze customer behavior and predict purchases to boost conversions and identify sales-ready leads.

Generative AI enables personalized medicine and early diagnosis to improve hospital efficiency, while computer vision powers robotic surgery, medical image and video recognition, and ethical, responsible use.

Explore zero-shot, few-shot, and chain of thought prompting to improve ai outputs, with context-based and iterative strategies, examples, and prompt optimization techniques for better multi-step reasoning.

Explore natural language processing fundamentals, covering NLP definitions, NLU and NLG, applications, evolution, common algorithms, challenges, and the NLP pipeline from data to model evaluation.

Explore practical NLP applications such as language detection and translation, smart replies, voice assistants, spell check, customer service bots, and spam filtering, with a hands-on Python demo.

Explore supervised and unsupervised NLP approaches, from Naive Bayes and SVM to deep learning transformers, with context-aware models for sentiment, spam detection, and translation.

Explore text classification and sentiment analysis in natural language processing using ML models like Naive Bayes, SVM, and Random Forest, with Transformers. Learn information retrieval and natural language queries.

Explore language detection and machine translation, from SMT and NMT to real-time translation and multilingual pipelines, and build conversational agents with NLU, dialogue management, and NLG using knowledge graphs.

Explore the six-stage nlp pipeline—from text acquisition and preprocessing to feature extraction, integration, and evaluation—using iPhone 14 reviews and practical data sourcing methods.

Apply an NLP pipeline to scrape amazon.in iPhone reviews, clean text, and extract sentiment using bag of words, tf-idf, and models like BERT.

Explore how large language models use transformers and deep neural networks, pre-train on vast data, and fine-tune for tasks, with applications, limitations, and ethical considerations.

Programmatically access OpenAI with Python by installing libraries, setting up API keys, and calling the chat completion endpoint in Colab. Submit prompts and receive responses from GPT-3.5 turbo.

Explore OpenAI LLM-based NLP tasks with hands-on prompts: summarize text, translate to Spanish, complete text, adjust temperature, and build a simple chatbot using GPT-3.5.

Deploy a web chatbot on an EC2 Linux VM with Docker and a PostgreSQL vector database. Upload, unzip, and run the chatbot via Colab notebook and a Streamlit app.