Master Python & Generative AI for Advanced Analytics

Explore how Python powers generative AI workflows with libraries like TensorFlow, PyTorch, Keras, and Scikit-Learn, covering data collection, preprocessing, model training, evaluation, deployment, and monitoring.

Install Python 3.9.2 on Windows and add it to path, verify the install, then start a Google Colab notebook to explore libraries with pip list for generative AI in analytics.

Explore variables as storage containers, practice descriptive naming, master assignment, and multiple assignment. Learn data types like integers, floats, strings, and booleans, and apply typecasting with int, float, and str.

Explore Python data structures, including lists, tuples, dictionaries, and sets, and learn indexing, slicing, mutability, and operations like append, remove, sort, and set unions, intersections, and differences.

Explore Python control flows, including conditional statements, for and while loops, and loop controls like break, continue, and pass, to build dynamic, responsive analytics programs.

Learn how to define and use functions in Python with def, creating modular, readable code that handles parameters, arguments, and return values, including positional, keyword, and default arguments.

Master object oriented programming in Python by exploring classes and objects, inheritance, polymorphism, abstraction, and encapsulation. Learn how constructors and methods shape modular, reusable code with real world entities.

Explore regular expressions in Python to validate, parse, and extract text using the re module. Define patterns with raw strings and use functions like search, match, findall, finditer, and sub.

Learn how Python modules organize code, import and reuse definitions, and leverage the standard library with os and pathlib to read and write files and manage paths.

Master Python file handling by reading and writing with open, read, and write methods; use r, rb, w, wb, and a, and apply csv module to read and write csv.

Explore error handling in Python by using try, accept, and finally blocks to manage exceptions, raise errors, and create custom exception classes.

Discover essential Python libraries for generative AI, including NumPy, Pandas, Matplotlib, Seaborn, TensorFlow, PyTorch, Keras, OpenCV, and Pillow for data, visualization, modeling, and image processing.

Learn numpy and pandas for data manipulation and preparation of generative AI models, covering arrays, data frames, series, CSV input, missing values, and grouping.

Visualize data with Matplotlib and Seaborn to understand distributions and patterns in generative AI. Create line, scatter, histogram, box, and pairplot visualizations, then customize styling for clear insights.

Explore image processing with OpenCV and Pillow to prepare and augment data for generative AI models. Apply pre-processing steps like resizing, rotating, and cropping to build training data for GANs.

Explore the scikit learn library for data preprocessing and baseline models, perform a train test split, train a random forest with 100 trees on the iris dataset, and evaluate accuracy.

Master Python and generative ai for advanced analytics by building and training neural networks with TensorFlow, Keras, and PyTorch.

Master Python and generative AI data wrangling to clean, transform, and organize raw data for analysis, covering loading, inspecting, handling missing values, filtering, merging, reshaping, and aggregating.

Load data with pandas from csv, excel, databases, or APIs; inspect, clean, merge, reshape, group, and aggregate while handling missing values, duplicates, and data type conversions for generative AI analytics.

Master advanced Python concepts for generative AI, including memory-efficient generators, decorators, and context managers. Apply asynchronous programming and advanced data structures from the collections module for scalable AI models.

Explore generative AI libraries that power building, training, and deploying models for text, images, music, and video. See how GANs, GPT models, and StyleGAN provide pre-trained options and streamlined workflows.

Explore generative adversarial networks, featuring a generator and discriminator, and a two-step training loop. Learn loss dynamics, mode collapse, and stability, with applications in images, data augmentation, art, and music.

Learn to construct and train a simple generative adversarial network in Python using TensorFlow and Keras, generating handwritten digits from the MNIST dataset with a generator and discriminator.

Train and optimize generative adversarial networks, balancing the generator and discriminator with loss functions, data augmentation, and regularization to produce realistic images.

Master the art of troubleshooting generative adversarial networks by diagnosing mode collapse, training instability, and vanishing gradients, and applying techniques like gradient penalties, learning rate scheduling, and evaluation metrics.

Evaluate model performance to ensure quality and diversity of GAN-generated data. Use inception score, Fréchet inception distance, and visual inspection to compare models and identify improvement areas.

Explore how generative AI enables data generation to create synthetic data that enhances training datasets, fills data gaps, and supports privacy in healthcare and finance for advanced analytics.

Explore data augmentation techniques such as random cropping, rotation, flipping, and color adjustments to expand datasets, boost model robustness, and improve generalization for advanced analytics.

Explore how generative AI enhances advanced text analysis, including text generation, sentiment analysis, translation, summarization, and entity recognition to boost customer service, brand insights, and decision making.

Leverage generative AI for advanced text analysis to automate responses, analyze sentiment, translate, summarize, and perform entity recognition, boosting efficiency and informed decision making.

Explore how generative AI transforms images and signals, using generative adversarial networks to create realistic images, enhance audio, and detect anomalies in data streams for medical, automotive, and design applications.

Generative AI enhances predictive analytics by combining with traditional models to generate scenarios and a range of plausible futures from historical data.

Explore how generative AI powers advanced analytics by creating synthetic data, predicting trends, and personalizing experiences across healthcare, finance, and marketing.

Build a generator and discriminator within a GAN to predict stock market trends, train on real and synthetic data, and generate synthetic stock prices while monitoring losses and accuracy.

Generate synthetic stock price data with a GAN, using np.random.normal noise and scaling back to the original range, then visualize volatility and trend patterns for data augmentation.

Evaluate a GAN-based model by plotting real versus generated stock prices and calculating the mean square error, then discuss data augmentation for stock market trend prediction.

Summarize the course structure, objectives, and key insights from Python and generative AI for advanced analytics, including GANs, applications in data generation, and stock market trend prediction.

Explore learning paths from beginner AI and Python courses to specialized generative models for advanced analytics, and access resources like Coursera, Udacity, edX, Generative Deep Learning, and Gans in Action.