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.

Explore Python data types such as integer, float, string, and boolean, and practice type conversion. Apply arithmetic and comparison operators in hands-on labs to reinforce concepts.

Explore Python lists and tuples with indexing and slicing, and list operations such as append, extend, pop, remove, and reverse, plus list comprehension; contrast mutable lists with immutable tuples.

Master dictionaries and key-value data access, including keys, values, items, and updates. Practice file handling with open modes and context managers, and write and call built-in and user-defined functions.

Explore Python arithmetic and comparison operators through hands-on examples in a Colab notebook, defining variables, performing addition, subtraction, multiplication, and division, and printing results.

Explore Python's comparison operators, including ==, !=, >, <, >=, and <=, with a=10 and b=20, true/false evaluations, and an assignment to reinforce learning for Generative AI for Developer course.

Demonstrate Python operators and assignment by printing comparisons among x, y, and z (10, 20, 30), showing x > y is false and y <= z is true.

Explore Python data types including integer, float, string, and complex with hands-on examples and guidance on when to use each type.

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 assignment one for Python data types by defining variables h, score, and name, printing their values, and showing int, float, and string types.

Explore Python lists with a practical e-commerce example, creating, indexing (including minus one), printing, and updating a products list with append, delete, and insert.

Learn to manipulate Python lists by extending new arrivals, slicing subsets, and printing inventory with multiple attributes, using append for a single item and extend for multiple items.

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.

Demonstrates Python tuples in a shopping inventory, showing that deletion is unsupported, while insertion and extension create new tuples, and contrasts tuples with lists using a static inventory example.

Learn how to use Python dictionaries for inventory management, including key-value pairs, using an inventory and prices dictionary, adding and updating items, calculating total value, and identifying low-stock items.

Explore Python dictionary operations for inventory management: remove zero stock items, merge inventories with summed quantities, and filter by categories like apparel, footwear, and accessories, with practical examples.

Explore Python dictionaries with an inventory example, filtering footwear items and printing their quantities. Update jeans and sneakers to new values and practice by typing the code three times.

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.

Learn to download data from urls using requests, parse csv with csv.reader and string io, and extract error messages from logs into an error file for ml workflows.

Explore Python inbuilt functions and user-defined functions, learn to use len, max, min, and range, and define reusable functions like greet for dynamic input in projects.

Learn to build and call Python functions to compute total prices from price and quantity, including float prices, integer quantities, and tax rate, in a cashier workflow.

Demonstrates creating and updating Python variables, printing values, and how overwriting assignments changes results. Explains data types with type for integers, floats, and strings.

Practice defining Python variables for price and quantity, compute subtotal as price times quantity, apply a 10% discount, and print subtotal, discount, and final total.

Explore the foundations of machine learning, including supervised, unsupervised, and reinforcement learning, and see how data preparation, model training, evaluation, and TensorFlow-driven workflows drive cat vs dog image classification.

Explore how machine learning powers voice assistants, autonomous vehicles, and personalized recommendations, using reinforcement learning and generative AI to improve accuracy and user experience.

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.

Reinforcement learning trains an agent to make driving decisions by exploring actions in a road environment, receiving rewards for safe and efficient behavior and penalties for errors.

Explore the differences between supervised, unsupervised, and reinforcement learning with examples like spam classification, sentiment analysis, and autonomous vehicles, and learn the key algorithms and tools used across these types.

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 linear regression as a supervised learning method for prediction, from simple one-variable models to multi-variable approaches, with practical examples and tools like scikit learn, pandas, and numpy.

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.

Improve model predictions by prioritizing data preparation and quality, and master cleaning techniques like handling missing values, normalization, feature encoding, and training and validation data splitting.

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.

Developers learn to mitigate bias and fairness. They also protect privacy and security while ensuring transparency and accountability in machine learning models.

Explore how machine learning integrates into devices and systems, from Copilot in office software to autonomous vehicles, and consider ethical and explainable AI and its impact on jobs and industry.

Learn numpy basics by converting a Python list to a numpy array, and using zeros, ones, arange, reshape, indexing, slicing, and min, max, sum operations.

Explore pandas fundamentals by building a dataframe from a Python list, saving and loading CSV data, and computing a discount price column for dynamic analytics.

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.

train the image classifier by rescaling and resizing training and validation data to 150 by 150, using flow from directory for cats and dogs across 15 epochs to improve accuracy.

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.

Learn how to generate synthetic e-commerce data in Colab with numpy and pandas, setting a reproducible seed, creating 500 customers with purchase amounts, categories, purchases, browsing times, cities, and views.

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.

Predict insurance premiums from age using single-variable linear regression with Python, numpy, and scikit-learn, applying data from arrays or csv and visualizing with seaborn.

learn to build a multi-variable linear regression model predicting insurance premium from age, height, and weight, including data loading, mean imputation, model fitting, and prediction.

Explore the fundamentals of generative AI, examine applications, live examples, and the behind-the-scenes tools and libraries, while covering ethics, privacy, security, and how to manage AI projects.

Define generative AI as a class of AI that creates text, images, music, or video by learning from data, tracing its evolution from the 1950s–60s foundations to modern neural networks.

Discover how generative AI can produce a complete video from a single prompt, including music, voiceover, fonts, and scenes for ads on YouTube, Facebook, and Instagram, using a freeware tool.

Explore how generative AI powers content creation, personalized education, healthcare, gaming, fashion, advertising, finance, legal drafting, robotics, urban planning, and personalized retail experiences.

Explore the technologies behind generative AI, including neural networks, CNN and RNN, GANs and VAEs, diffusion and autoregressive models, transformer models, reinforcement learning, and fine tuning and transfer learning.

Demo shows generating a complete PowerPoint deck from a single prompt using the gamut app, with automatic slide titles, images, and animations, exportable as PDF or PPT.

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.

Explore real-world GenAI security attacks, from adversarial and vision attacks to poisoning, privacy, and backdoor attacks during the training phase, and learn why clean training data and robust defenses matter.

Explore generating AI voices for scripts using a nearly 100-language toolkit with male and female voices, sign up with email or Google, choose voices, and download audio for narration.

Explore Gen AI in finance, from fraud detection and anomaly detection to predictive modeling on historical data. Enable automated financial reporting and intelligent process automation for faster, accurate insights.

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

Explore how GenAI powers HR with automated resume screening, personalized candidate assessments, intelligent resume parsing, attrition prediction, and data-driven performance management for fair, unbiased evaluations.

Demonstrate generating ai avatar videos using ai studio to clone yourself and customize an avatar. Script with ChatGPT and render the video, creating an ai news broadcast by the avatar.

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.

Master prompt engineering by crafting effective prompts, iteratively refining tasks and outputs, and using a data-driven approach to unlock AI potential, improve output quality, productivity, and creativity.

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 the behind-the-scenes journey from prompt to output, detailing input encoding into tokens, model initialization, and context processing, followed by token generation and output assembly.

Explore practical prompting with ChatGPT-3.5 using prompts for everyone, including content generation, constraints, translation, scripting, slides, Excel formulas, and role-based prompts across Python, health, and business planning.

Explore how to use GPT-4 to search the web, upload PDFs, summarize documents, generate code and data, and create social media content with SEO-ready outputs.

Demonstrate prompting with Anthropic Claude on cloud to access up-to-date data and generate, optimize, and debug Python and web apps, with developer prompts.

Learn how to prompt Google Gemini within the Google ecosystem, including signing up with a free Gmail id and using prompts for code, translation, and blogs. Note free version limits.

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.

Trace NLP's evolution from heuristic rule-based methods to machine learning and deep learning with neural networks, then explore keyword search, rule-based parsing, and regex in practice.

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 challenges in natural language processing, including ambiguity, contextual words, sarcasm and tone, irony, spelling errors, and language diversity across dialects and slang.

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 text summarization and topic modeling, including extractive and abstractive methods, BERT, GPT, LDA, NMF, and HDP, plus text generation and speech-to-text for real-world applications.

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 text classification to filter emails as spam or not spam using Google Colab, preprocessing with NLTK stopwords and tokenization, and model training with a count vectorizer.

Learn to generate a three-line summary from large text by using Colab, nltk, and sumy LSA summarizer, with tokenization and stop-word removal.

Learn to cleanse data for NLP preprocessing by removing emojis, urls, HTML tags, punctuation, and spelling errors; normalize case and apply basic text corrections with Python.

Explore the fundamentals of large language models, how they work, and their industry applications. Learn to use Hugging Face, fine-tune and customize models, and examine multimodal LM trends.

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.

Explore how LMS powers creative and practical tasks, from text-to-image and image-to-image generation to text-to-video, motion, music, code, translations, sentiment analysis, and AI chatbots.

Explore custom versus fine-tune language models, define objectives, collect and preprocess data, then train and evaluate using hugging face transformers and pre-trained models for tasks like code writing and translation.

Explore how multimodal models integrate text, images, and audio to generate scripts, images, and voices for video, healthcare, entertainment, and education.

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.

Learn to access Anthropic Claude programmatically with Python in Colab, configure API keys, and use client.message_create to generate prompts and build AI applications.

Demonstrates Claude LLM NLP tasks on the anthropic cloud, including text summarization and translation to French. Builds a Python chatbot and shows API key and client setup.

Fine-tune a distilbert model on a labeled sms dataset using huggingface in Colab to classify messages as harmful, spam, or generic, then deploy a text classification pipeline.

Learn to build an OpenAI chatbot with a flask web interface, including templates, static files, and a .env OpenAI key, then debug and deploy on OS with Python and pip.

upload a pdf to a chatbot, then ask questions to summarize or extract information from the document using a flask app and OpenAI GPT-3.5 turbo.

Build a text-based chat bot by sending prompts to OpenAI's GPT-3.5 turbo, convert the replies to speech with a text-to-speech tool, and playback audio locally.

Provision a VM with two CPUs, four gigs of ram, and 40 GB disk, or use a capable laptop. Install Python 3 and pip3 and connect via SSH to begin.

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.

Learn to use an OpenAI API key to train a chatbot on a target website, crawl content, build vector DB chunks for Postgres, and answer questions such as admission deadlines.

Build a telegram bot powered by OpenAI that answers prompts via ChatGPT in Telegram, using BotFather to obtain a token, install python-telegram-bot, and run a GPT-3.5 chat with conversation memory.

Explore Hugging Face and its transformer library to access hundreds of pre-trained NLP models for text classification, summarization, and named entity recognition, with pipelines for text generation using GPT-2.

Discover how transformers and Hugging Face pipelines enable NLP tasks such as text generation, sentiment analysis, translation, summarization, question answering, automatic speech recognition, and named entity recognition.