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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 the relational model and write SQL: CREATE TABLE, INSERT, SELECT, UPDATE and DELETE, string patterns, sorting and grouping result sets, and inner and outer joins.
Learn supervised and unsupervised machine learning in Python: K-nearest neighbors, decision trees, random forests, regression evaluation, K-means and hierarchical clustering, dimensionality reduction, and collaborative filtering.
Deploy deep learning networks on GPU-accelerated hardware: use Nvidia GPUs, TPUs, and IBM Power AI to speed up training and inference for image and video classification.
Build bar charts, histograms, scatter plots, word clouds, radar and waffle charts, maps, and interactive Shiny apps in R to present data meaningfully.
Run your first Docker containers, build and push images to a registry, and orchestrate services with Docker Swarm using scaling and rolling updates.
Learn how open source software works and contribute to projects: find the right project, use Git and GitHub, and host, govern, and lead your own.
Extract structured data from text: entity, relation and event extraction, sentiment analysis, and hands-on writing of declarative information extractors for social media, healthcare and finance.
Learn Scala's object-oriented and functional sides, then use Apache Spark RDDs, DataFrames and machine learning pipelines to fit models and search for optimal hyperparameters on a cluster.
Learn Python from scratch: work with lists, dictionaries, loops, functions, and classes, then load data with Pandas, use NumPy arrays, and call simple APIs.
Follow a methodology for data science problems: form a concrete business or research question, collect and analyze data, build a model, and act on post-deployment feedback.
Get oriented in deep learning: compare convolutional, recurrent, autoencoder and Boltzmann-based networks, tackle the vanishing gradient, and survey platforms like H2O.ai, Theano, Torch and Caffe.
Learn the core concepts of cloud computing: its definition, history and business case, the IaaS/PaaS/SaaS service models, public/private/hybrid deployment models, and architecture components.
Build Oozie workflows in XML with forks, joins, and case statements, schedule them with the Oozie coordinator, and compare MapReduce v1 with the YARN model.
Hear practitioners define data science: career paths into the field, R versus Python, everyday tools, business use cases, and how companies hire data scientists.
Explore IBM Cloud end to end: configure identity and access management, compare IaaS and container deployment options, survey database, AI, Blockchain and IoT services, and deploy an app.
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