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Cognitive Class is an online learning platform offering a range of courses and resources to help people become data and AI-ready.
Learn SQL basics, relational databases, and advanced concepts through hands-on exercises. Master essential SQL statements, join operations, and data manipulation techniques for effective database management.
Master data visualization in R: create compelling charts, plots, and interactive maps to effectively communicate insights and tell data-driven stories.
Explore machine learning basics, algorithms, and models using Python. Learn supervised and unsupervised techniques, statistical modeling, and real-world applications. Gain practical skills in classification, regression, clustering, and dimensionality red…
Learn Docker container basics, create and manage images, and explore orchestration with Docker Swarm through hands-on labs. Gain practical skills for efficient application deployment and management.
Learn TensorFlow for deep learning: from basics to advanced architectures. Master neural networks, CNNs, RNNs, and autoencoders. Practical applications in image, sound, and text processing.
Learn to integrate Watson AI services into Swift mobile apps, enhancing functionality with cognitive capabilities like visual recognition and natural language processing.
Learn to leverage GPU hardware for faster deep learning, deploying networks for image and video classification using platforms like IBM Power AI with NVIDIA GPUs.
Learn to secure cloud applications with encryption, authentication, and authorization techniques using IBM Cloud services.
Learn to contribute to open source projects, from finding the right project to using tools like Git and GitHub. Gain skills to create and lead your own open source initiatives.
Learn to create AI-powered APIs using Watson and Swift, and explore strategies for monetizing your creations in this practical guide.
Learn to connect R with databases, perform SQL operations, and leverage in-database analytics. Ideal for data scientists and SQL professionals looking to enhance their skills in handling large datasets.
Learn to build, containerize, and deploy scalable web applications on Kubernetes, covering local development, custom domains, monitoring, and scaling techniques.
Learn techniques to reduce dataset features using PCA and EFA, with hands-on practice in R. Explore the theory and application of dimensionality reduction in machine learning.
Learn predictive modeling fundamentals, including CRISP-DM methodology, common techniques, and hands-on practice with IBM SPSS Modeler to solve real-world data challenges.
Expand knowledge on RDDs, Spark architecture, data distribution, and task parallelization. Learn advanced API operations and optimize data for joins using memory caching.
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