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The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.

In the course "Artificial Intelligence in Social Media Analytics", learners will explore the intersection of artificial intelligence and social media analytics, equipping them with essential skills to navigate and analyze digital landscapes. By delving into machine learning fundamentals, natural language processing, sentiment analysis, and topic modeling, participants will gain practical experience in applying AI techniques to real-world social media data. This course stands out by providing not only theoretical insights but also hands-on opportunities to construct classifiers, perform sentiment analysis, and build semantic networks, all tailored to the complexities of social media content. As learning progress, they will develop a keen understanding of how AI can uncover hidden patterns, sentiment, and topics within vast amounts of unstructured data. The unique blend of fundamental concepts and practical applications ensures that participants can effectively analyze social media interactions and derive actionable insights. Whether for career advancement or personal interest, this course offers a comprehensive toolkit to leverage AI for understanding social dynamics and enhancing engagement strategies in digital platforms.

The course, "Introduction to Social Computing" offers a comprehensive exploration of the intersection between technology and society, equipping learners with essential skills in social media analytics and influence. By covering a range of topics from data pre-processing to feature extraction and model evaluation, students will gain practical experience in applying machine learning techniques to real-world social media scenarios. Through hands-on modules, learners will delve into the dynamics of socio-technical systems and responsible AI, understanding how digital platforms shape human interactions and behaviors. The unique combination of theoretical insights and practical applications prepares students to navigate the complexities of social media, including analyzing firestorms and mitigating misinformation. What sets this course apart is its focus on gamification and cognitive biases in online environments, providing students with innovative strategies to enhance user engagement and promote critical thinking. Whether you are looking to advance your career in tech or simply understand the social implications of technology, this course will empower you to effectively analyze and leverage social computing in today's digital landscape.

Dive into the fascinating journey of artificial intelligence, from its theoretical beginnings to today's powerful generative models. This course offers a unique perspective on how AI has transformed over decades, highlighting the crucial developments in deep learning that paved the way for modern AI breakthroughs. After completing this course, you will be able to: - Trace the evolution of AI from its early concepts to modern generative models - Identify key technological breakthroughs that enabled the rise of generative AI - Understand the fundamental differences between traditional AI and generative AI - Recognize potential applications and impacts of generative AI across various fields You'll gain insights into the game-changing advancements—like self-supervised learning and transformers—that propelled generative AI to its current prominence. By understanding this history, you'll be better equipped to anticipate future trends and applications in AI. Whether you're an AI enthusiast, a professional looking to leverage AI in your field, or simply curious about the technology shaping our future, this course provides valuable context to help you navigate the exciting world of generative AI.

Dive into the world of generative AI and learn how to select the right model for your needs in this practical course. You'll gain a solid understanding of how generative AI models work and compare deployment options like web APIs, hosted solutions, and local installations. By the end of this course, you will be able to: • Describe the basic architecture of generative AI models • Compare different AI model deployment options • Evaluate AI models using benchmarks and custom assessments • Troubleshoot and improve model performance • Determine when to use in -context learning vs. retrieval augmented generation Through hands-on exercises, you'll learn to evaluate models using industry benchmarks and create custom assessments for your specific use cases. You'll also master techniques to troubleshoot and enhance model performance. What sets this course apart is its focus on real-world application - you'll leave equipped to make informed decisions about AI model selection and optimization for your projects. Whether you're new to AI or looking to deepen your knowledge, this course will empower you to leverage generative AI effectively.

In this engaging course, you'll learn how to identify and define the goals and scope of your Social Impact Assessments (SIA), map out the social, environmental, and economic impacts, and accurately value the outcomes. Discover how to quantify the true value of your projects using the Social Return on Investment (SROI) methodology and unlock the potential for positive change. This course is tailored for a diverse group of professionals, including program managers, project leaders, social impact analysts or evaluators, procurement practitioners, and finance and accounting professionals. It also caters to stakeholder engagement or communication specialists and anyone involved in the management or decision-making of these disciplines. These individuals will benefit from learning how to conduct thorough social impact assessments and apply the Social Return on Investment (SROI) methodology in their respective fields. To gain the most from this course, participants should have a basic understanding of social value concepts and be familiar with evaluation principles. This fundamental knowledge will enable learners to grasp the more advanced topics covered in the course and effectively apply the SROI methodology to their projects. After completing this course, learners will be able to define and explain the concept of social value and describe the principles of the SROI methodology. They will also be equipped to interpret and communicate SROI results effectively. Additionally, participants will gain the skills to apply the concepts learned in real-world scenarios, enhancing their ability to create and measure social impact through their projects and initiatives.

In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing. You'll gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in using platforms like Amazon Mechanical Turk (AMT) for crowdsourced tasks. This unique approach combines theoretical knowledge with hands-on experience, allowing you to implement Inter-Annotator Agreement (IAA) techniques to ensure high-quality annotated data. By completing this course, you will be well-equipped to design and conduct impactful crowdsourcing studies, improving AI models in real-world applications such as healthcare and research. Whether you're looking to enhance your skills in machine learning, optimize data collection processes, or understand the ethical implications of crowdsourcing, this course offers invaluable insights and tools.

The course "Chatbots" offers a deep dive into the world of chatbots, equipping learners with the skills to design, build, and optimize conversational interfaces. You will explore the evolution of chatbot technology and understand the fundamental mechanics that drive their functionality. Through hands-on projects using Amazon Lex and AWS, you'll not only learn to create chatbots but also how to evaluate their performance using machine learning classifiers. What sets this course apart is its practical approach, allowing you to apply theoretical knowledge in real-world scenarios. Collaborating with peers, you'll tackle challenges together, enhancing your problem-solving skills while fostering a supportive learning environment. By the end of the course, you'll have the confidence to develop functional chatbots tailored for various applications, from customer service to personal assistants. Whether you are a novice looking to enter the tech field or an experienced professional aiming to expand your skill set, this course provides invaluable insights and practical tools to advance your career in the rapidly growing chatbot landscape. Join us to unlock the potential of conversational AI!

The "Foundations of Strategic Communications" course equips learners with the skills to master professional, technical, and leadership communication strategies. By completing this course, participants will be able to analyze audiences, craft effective messages, and adapt their communication style to fit organizational cultures. Key learning outcomes include enhancing emotional intelligence, improving listening and nonverbal communication, and overcoming common communication barriers. Learners will also develop leadership communication skills, enabling them to influence and foster collaboration in both team settings and external engagements. What makes this course unique is its practical focus on applying communication models in real-world scenarios. Learners will gain the confidence to manage diverse audiences and complex workplace dynamics. The course emphasizes tailoring communication strategies based on audience analysis, ensuring learners are prepared for a variety of professional environments. By the end of the course, participants will have the tools to communicate persuasively, lead with influence, and foster stronger workplace relationships. This course provides a competitive advantage in both career growth and personal development by empowering learners with the ability to strategically communicate in any setting.

The course "Advanced Malware and Network Anomaly Detection" equips learners with essential skills to combat advanced cybersecurity threats using artificial intelligence. This course takes a hands-on approach, guiding students through the intricacies of malware detection and network anomaly identification. In the first two modules, you will gain fundamental knowledge about various types of malware and advanced detection techniques, including supervised and unsupervised learning methods. The subsequent modules shift focus to network security, where you'll explore anomaly detection algorithms and their application using real-world botnet data. What sets this course apart is its emphasis on practical, project-based learning. By applying your knowledge through hands-on implementations and collaborative presentations, you will develop a robust skill set that is highly relevant in today's cybersecurity landscape. Completing this course will prepare you to effectively identify and mitigate threats, making you a valuable asset in any cybersecurity role. With the rapid evolution of cyber threats, this course ensures you stay ahead by leveraging the power of AI for robust cybersecurity measures.

In the course "Securing AI and Advanced Topics", learners will delve into the cutting-edge intersection of AI and cybersecurity, focusing on how advanced techniques can secure AI systems against emerging threats. Through a structured approach, you will explore practical applications, including fraud prevention using cloud AI solutions and the intricacies of Generative Adversarial Networks (GANs). Each module builds upon the previous one, enabling a comprehensive understanding of both offensive and defensive strategies in cybersecurity. What sets this course apart is its hands-on experience with real-world implementations, allowing you to design effective solutions for detecting and mitigating fraud, as well as understanding adversarial attacks. By evaluating AI models and learning reinforcement learning principles, you will gain insights into enhancing cybersecurity measures. Completing this course will equip you with the skills necessary to address complex challenges in the evolving landscape of AI and cybersecurity, making you a valuable asset in any organization. Whether you are seeking to deepen your expertise or enter this critical field, this course provides the tools and knowledge you need to excel.

Fine-tuning a large language model (LLM) is critical for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring gen AI engineers valuable fine-tuning skills employers are actively seeking. During this course, you'll explore different approaches to fine-tuning and causal LLMs with human feedback and direct preference. You'll look at LLMs as policies for probability distributions for generating responses and the concepts of instruction-tuning with Hugging Face. You'll learn to calculate rewards using human feedback and reward modeling with Hugging Face. Plus, you'll explore reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO) and PPO Trainer, and optimal solutions for direct preference optimization (DPO) problems. As you learn, you'll get valuable hands-on experience in online labs where you'll work on reward modeling, PPO, and DPO. If you're looking to add in-demand capabilities in fine-tuning LLMs to your resume, ENROLL TODAY and build the job-ready skills employers are looking for in just two weeks!