AI for Data Science

This comprehensive course delves deep into the fascinating world of Generative AI. Through a combination of engaging lectures and hands-on practice, participants will gain an in-depth understanding of what generative models are, how they differ from other AI techniques, and the theories and principles underlying them. You will discover various types of generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), and explore the process involved in training these models. Then you will examine the strengths, limitations, and practical applications of generative models across various domains, such as image generation, text generation, and data augmentation. Next, you will learn how to evaluate the performance of generative models and focus on ethical considerations in generative AI and the potential societal impact of these technologies. Finally, you will have the opportunity to generate synthetic data using generative models for training and testing purposes and investigate the notion of responsible AI in the generative era. Upon course completion, you will be prepared not just to use these powerful tools, but to use them wisely and ethically.

This course dives deep into the world of generative models, providing learners with a comprehensive understanding of various generative techniques and their applications. This course is carefully designed to bridge theoretical concepts with practical applications, demystifying the methods used in popular generative models like generative adversarial networks (GANs), variational autoencoders (VAEs), and more. Through a combination of rich imagery, illustrative examples, and detailed explanations, participants will explore the differences between generative and discriminative modeling, the foundational framework of generative artificial intelligence (AI), and the various evaluation metrics that gauge the success of these models. Whether you're a budding data scientist, an AI enthusiast, or a seasoned researcher, this course offers a deep dive into the cutting-edge techniques that are shaping the future of artificial intelligence.

Dive deep into the expansive realm of large language models (LLMs), a pivotal cornerstone in today's artificial intelligence (AI)-driven landscape. This course unravels the intricacies of these models, from their architecture and training methods to their profound implications in real-world scenarios. Begin by exploring the significance of LLMs in the world of AI. Then you will examine the architecture of LLMs, evaluate the impact of data on the effectiveness of LLMs, and fine-tune your LLM for a specific task. Next, you will investigate the ethical implications of using LLMs, including potential biases and privacy issues. Finally, you will discover the potential and limitations of LLMs and learn how to stay updated with the latest advancements in this dynamic field.

In the modern digital era, generative artificial intelligence (AI) emerges as a game-changer, introducing unprecedented capabilities to the business landscape. This course is tailored for professionals seeking to understand the depth and breadth of generative AI's impact on the business world. Dive into the essentials, from the foundational concepts to ethical ramifications and real-world implementations. You will explore the transformative potential of generative AI on business operations, products, and customer experiences and delve into the algorithms propelling these innovations. Discover the possibilities for the interplay of human expertise with AI, managing data for AI deployments, and navigating legal landscapes. At the end of the course, participants will be adept at assessing the business value of generative AI and equipped with the knowledge to strategically integrate it into their organization's digital evolution.

This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.

Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.

Generative Pre-trained Transformer (GPT) models are advanced artificial intelligence (AI) systems designed to understand and generate human-like text based on the information they've been trained on. These models can perform a wide range of language tasks, from writing stories to answering questions, by learning patterns in vast amounts of text data. In this course, you will dive into the world of GPT models and the foundational models that are pivotal to the development of the GPT-n series. You will gain an understanding of the terminology and concepts that make GPT models outstanding in performing natural language processing tasks. Next, you will explore the concept of attention in language models and explore the mechanics of the Transformer architecture, the cornerstone of GPT models. Finally, you will explore the details of the GPT model. You will discover methods used to adapt these models for particular tasks through supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and techniques such as prompt engineering and prompt tuning.

Autoencoders are a class of artificial neural networks employed in unsupervised learning tasks, primarily focused on data compression and feature learning. Begin this course off by exploring autoencoders, learning about the functions of the encoder and the decoder in the model. Next, you will learn how to create and train an autoencoder, using the Google Colab environment. Then you will use PyTorch to create the neural networks for the autoencoder, and you will train the model to reconstruct high-dimensional, grayscale images. You will also use convolutional autoencoders to work with multichannel color images. Finally, you will make use of the denoising autoencoder, a type of model that takes in a corrupted image with Gaussian noise, and attempts to reconstruct the original clean image, thus learning better representations of the input data. In conclusion, this course will provide you with a solid understanding of basic autoencoders and their use cases.

Variational autoencoders (VAEs) represent a powerful variant of traditional autoencoders, designed to address the challenge of generating new and diverse samples from the learned latent space. VAEs introduce probabilistic components, incorporating a probabilistic encoder that maps input data to a distribution in the latent space and a decoder that reconstructs data from samples drawn from this distribution. Begin this course by discovering how variational autoencoders can be used for generating images. Next, you will create and train VAEs in Python and the Google Colab environment. Then you will construct the encoder and decoder. Finally, you will train the VAE on multichannel color images. Upon course completion, you will have a solid understanding of variational autoencoders and their use in generating images.

Generative adversarial networks (GANs) represent a revolutionary approach to generative modeling within the realm of artificial intelligence. Begin this course by discovering GANs, including the basic architecture of a GAN, which involves two neural networks competing in a zero-sum game - the generator and the discriminator. Next, you will explore how to construct and train a GAN using the PyTorch framework to create and train the models. You will define the generator and discriminator separately, and then kick off the model training. Finally, you will focus the deep convolutional GAN, which uses deep convolutional neural networks (CNNs) rather than regular neural networks. CNNs are optimized for working with grid-like data, such as images and these can generate better-quality images than GANs built using dense neural networks. In conclusion, this course will provide you with a strong understanding of generative adversarial networks, their architecture, and their usage scenarios.

Deep learning has revolutionized natural language processing (NLP), offering powerful techniques for understanding, generating, and processing human language. Through deep neural networks (DNNs), NLP models can now comprehend complex linguistic structures, extract meaningful information from vast amounts of text data, and even generate human-like responses. Begin this course by learning how to utilize Keras and TensorFlow to construct and train neural networks. Next, you will build a DNN to classify messages as spam or not. You will find out how to encode data using count vector and term frequency-inverse document frequency (TF-IDF) encodings via the Keras TextVectorization layer. To enhance the training process, you will employ Keras callbacks to gain insights into metrics tracking, TensorBoard integration, and model checkpointing. Finally, you will apply sentiment analysis using word embeddings, explore the use of pre-trained GloVe word vector embeddings, and incorporate convolutional layers to grasp local text context.

Recurrent neural networks (RNNs) are a class of neural networks designed to efficiently process sequential data. Unlike traditional feedforward neural networks, RNNs possess internal memory, which enables them to learn patterns and dependencies in sequential data, making them well-suited for a wide range of applications, including natural language processing. In this course, you will explore the mechanics of RNNs and their capacity for processing sequential data. Next, you will perform sentiment analysis with RNNs, generating and visualizing word embeddings through the TensorBoard embedding projector plug-in. You will construct an RNN, employing these word embeddings for sentiment analysis and evaluating the RNN's efficacy on a set of test data. Then, you will investigate advanced RNN applications, focusing on long short-term memory (LSTM) and bidirectional LSTM models. Finally, you will discover how LSTM models enhance the processing of long text sequences and you will build and train a bidirectional LSTM model to process data in both directions and capture a more comprehensive understanding of the text.

Transfer learning is a powerful machine learning technique that involves taking a pre-trained model on a large dataset and fine-tuning it for a related but different task, significantly reducing the need for extensive datasets and computational resources. Transformers are groundbreaking neural network architectures that use attention mechanisms to efficiently process sequential data, enabling state-of-the-art performance in a wide range of natural language processing tasks. In this course, you will discover transfer learning, the TensorFlow Hub, and attention-based models. Then you will learn how to perform subword tokenization with WordPiece. Next, you will examine transformer models, specifically the FNet model, and you will apply the FNet model for sentiment analysis. Finally, you will explore advanced text processing techniques using the Universal Sentence Encoder (USE) for semantic similarity analysis and the Bidirectional Encoder Representations from Transformers (BERT) model for sentence similarity prediction.

Attention mechanisms in natural language processing (NLP) allow models to dynamically focus on different parts of the input data, enhancing their ability to understand context and relationships within the text. This significantly improves the performance of tasks such as translation, sentiment analysis, and question-answering by enabling models to process and interpret complex language structures more effectively. Begin this course by setting up language translation models and exploring the foundational concepts of translation models, including the encoder-decoder structure. Then you will investigate the basic translation process by building a transformer model based on recurrent neural networks without attention. Next, you will incorporate an attention layer into the decoder of your language translation model. You will discover how transformers process input sequences in parallel, improving efficiency and training speed through the use of positional and word embeddings. Finally, you will learn about queries, keys, and values within the multi-head attention layer, culminating in training a transformer model for language translation.

Hugging Face, a leading company in the field of artificial intelligence (AI), offers a comprehensive platform that enables developers and researchers to build, train, and deploy state-of-the-art machine learning (ML) models with a strong emphasis on open collaboration and community-driven development. In this course, you will discover the extensive libraries and tools Hugging Face offers, including the Transformers library, which provides access to a vast array of pre-trained models and datasets. Next, you will set up your working environment in Google Colab. You will also explore the critical components of the text preprocessing pipeline: normalizers and pre-tokenizers. Finally, you will master various tokenization techniques, including byte pair encoding (BPE), Wordpiece, and Unigram tokenization, which are essential for working with transformer models. Through hands-on exercises, you will build and train BPE and WordPiece tokenizers, configuring normalizers and pre-tokenizers to fine-tune these tokenization methods.

Sentiment analysis, named entity recognition (NER), question answering, and text generation are pivotal tasks in the realm of Natural Language Processing (NLP) that enable machines to interpret and understand human language in a nuanced manner. In this course, you will be introduced to the concept of Hugging Face pipelines, a streamlined approach to applying pre-trained models to a variety of NLP tasks. Through hands-on exploration, you will learn how to classify text using zero-shot classification techniques, perform sentiment analysis with DistilBERT, and apply models to specialized tasks, utilizing the power of NLP to adapt to niche domains. Next, you will discover how to employ models to accurately answer questions based on provided contexts and understand the mechanics behind model-based answers, including their limitations and capabilities. Finally, you will discover various text generation strategies such as greedy search and beam search, learning how to balance predictability with creativity in generated text. You will also explore text generation through sampling techniques and the application of mask filling with BERT models.

Language translation, text summarization, and semantic textual similarity are advanced problems within the field of Natural Language Processing (NLP) that are increasingly solvable due to advances in the use of large language models (LLMs) and pre-trained models. In this course, you will learn to translate text between languages with state-of-the-art pre-trained models such as T5, M2M 100, and Opus. You will also gain insights into evaluating translation accuracy with BLEU scores and explore multilingual translation techniques. Next, you will explore the process of summarizing text, utilizing the powerful BART and T5 models for abstractive summarization. You will see how these models extract and generate key information from large texts and learn to evaluate the quality of summaries using ROUGE scores. Finally, you will master the computation of semantic textual similarity using sentence transformers and apply clustering techniques to group texts based on their semantic content. You will also learn to compute embeddings and measure similarity directly.

Fine-tuning in the context of text-based models refers to the process of taking a pre-trained model and adapting it to a specific task or dataset with additional training. This technique leverages the general language understanding capabilities acquired by the model during its initial extensive training on a large corpus of text and refines its abilities to perform well on a more narrowly defined task or domain-specific data. In this course, you will learn how to fine-tune a model for sentiment analysis, starting with the preparation of datasets optimized for this purpose. You will be guided through setting up your computing environment and preparing a BERT classifier for sentiment analysis. Next, you will discover how to structure text data and align named entity recognition (NER) tags with subword tokenization. You will build on this knowledge to fine-tune a BERT model specifically for NER, training it to accurately identify and classify entities within text. Finally, you will explore the domain of question answering, learning to handle the challenges of long contexts to extract precise answers from extensive texts. You will prepare QnA data for fine-tuning and utilize a DistilBERT model to create an effective QnA system.

Causal language modeling (CLM), text translation, and summarization demonstrate the versatility and depth of language understanding and generation by artificial intelligence (AI). Fine-tuning models help improve the performance of models for these specific tasks. ​In this course, you will explore CLM with DistilGPT-2 and masked language modeling (MLM) with DistilRoBERTa, learning how to prepare, process, and fine-tune models for generating and predicting text. Next, you will dive into the nuances of language translation, focusing on translating English to Spanish. You will prepare and evaluate training data and learn to use BLEU scores for assessing translation quality. You will fine-tune a pre-trained T5-small model, enhancing its accuracy and broadening its linguistic capabilities. Finally, you will explore the intricacies of text summarization. Starting with data loading and visualization, you will establish a benchmark using the pre-trained T5-small model. You will then fine-tune this model for summarization tasks, learning to condense extensive texts into succinct summaries.