Why AI Courses?

Overall Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Furthermore The exam can be taken at a testing center or from the comfort and convenience of a home or office location as an online proctored exam.

Part-Time

Fast forward your career in the IT industry with a part-time course at School of IT. In turn Part-time courses allow working professionals to transition into a new skill set while working. Moreover at School of IT we are agile and customize a course to the individual.

Full Time

Ready to start a career in IT? Use the Microsoft Azure platform as a full time student at School of IT. Thus beginning your career in Artificial Intelligence.

High School

Learn about the Microsoft Azure platform and prepare for the future while you’re still in high school. Thus no matter where you are, we come to you! Thus giving you the analytical skills to pursue your dreams!

Corporate

Learn about AWS and up skill yourself or your company while you’re working. Thus no matter where you are, we come to you and give the tools to move up in your company.

Course Objectives.

All in all Learn the essential fundamentals of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).

By the end of the AI Programming with Python Fundamentals, students will have usable knowledge of the following:

• Overall Introduction to Python.
• Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib.
• Thus Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices.
• Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.
• Furthermore Gain a solid foundation in the hottest fields in AI: neural networks, deep learning, and PyTorch.

Course Objectives.

By the end of the Azure AI Fundamentals Course, students will have usable knowledge of the following:

• Overall Describe AI workloads and considerations.
• Get started with AI on Azure
• Create no-code predictive models with Azure Machine Learning.
• Use automated machine learning in Azure Machine Learning.
• Create a Regression Model with Azure Machine Learning designer.
• Create a classification model with Azure Machine Learning designer.
• In turn Create a Clustering Model with Azure Machine Learning designer.
• Explore computer vision in Microsoft Azure.
• Analyze images with the Computer Vision service.
• Classify images with the Custom Vision service.
• Detect objects in images with the Custom Vision service.
• Detect and analyze faces with the Face service.
• Read text with the Computer Vision service.
• Analyze receipts with the Form Recognizer service.
• Explore natural language processing.
• Analyze text with the Text Analytics service.
• Recognize and synthesize speech.
• Translate text and speech.
• Create a language model with Language Understanding.
• Explore conversational AI.
• Build a bot with QnA Maker and Azure Bot Service.

Course Objectives.

By the end of the Azure AI Engineer Associate Course, students will have usable knowledge of the following:

• Overall Evaluate text with Azure Cognitive Language Services.
• All in all Classify and moderate text with Azure Content Moderator.
• Be able to Add conversational intelligence to your apps by using Language Understanding Intelligent Service (LUIS).
• features of computer vision workloads on Azure.
• Discover sentiment in text with the Text Analytics API.
• In turn Process and Translate Speech with Azure Cognitive Speech Services.
• Thus Transcribe speech input to text.
• Synthesize Text Input to Speech.
• Translate speech with the speech service.
• Create Intelligent Bots with the Azure Bot Service.
• in turn Build a bot with QnA Maker and Azure Bot Service.
• Process and classify images with the Azure cognitive vision services.
• Identify faces and expressions by using the Computer Vision API in Azure Cognitive Services.
• Process images with the Computer Vision service.
• Classify images with the Microsoft Custom Vision Service.
• Thus Evaluate the requirements for implementing the Custom Vision APIs.
• All in all Extract insights from videos with the Video Indexer service.

Course Objectives.

By the end of the Azure Data Engineer Associate Course, students will have usable knowledge of the following:

• Overall learn Azure for the Data Engineer.
• Understand the evolving world of data.
• All in all Survey the services on the Azure Data platform.
• Identify the tasks of a data engineer in a cloud-hosted architecture.
• Store data in Azure
• In turn Choose a data storage approach in Azure.
• Thus Create an Azure Storage account.
• Connect an app to Azure Storage.
• Secure your Azure Storage account.
• Store application data with Azure Blob storage.
• Furthermore Work with relational data in Azure.
• Provision an Azure SQL database to store application data.
• Migrate your relational data stored in SQL Server to Azure SQL Database.
• Introduction to Azure Database for PostgreSQL.
• Thus Scale multiple Azure SQL Databases with SQL elastic pools.
• Secure your Azure SQL Database.
• In turn Develop and configure an ASP.NET application that queries an Azure SQL database.
• Work with NoSQL data in Azure Cosmos DB.
• Create an Azure Cosmos DB database built to scale.
• Thus Choose the appropriate API for Azure Cosmos DB.
• Insert and query data in your Azure Cosmos DB database.
• All in all Store and access graph data in Azure Cosmos DB with the Graph API.
• Store and Access NoSQL Data with Azure Cosmos DB and the Table API.
• Build a .NET Core app for Azure Cosmos DB in Visual Studio Code.
• Optimize the performance of Azure Cosmos DB by using partitioning and indexing strategies.
• In turn Distribute your data globally with Azure Cosmos DB.
• Large-Scale Data Processing with Azure Data Lake Storage Gen2.
• Introduction to Azure Data Lake storage.
• Upload data to Azure Data Lake Storage.
• Secure your Azure Storage account.

Data Science Courses Objectives.

By the end of the data management course, students will have usable knowledge of the following:

• Overall apply statistical methods to data
• Be able to understand Python or programming in R to explore and transform data.
• Understand and create, validate machine learning models with Azure Machine Learning.
• Thoroughly Understand Python/Programming in R code to build machine learning models.
• Apply data science techniques to common scenarios.
• Introduction to compute targets.
• Acquire the skills needed to find structure in data and how to use the python libraries.
• Matplotlib.
• SciKit Learn.
• NumPy.
• NLTK,
• Implement a machine learning solution for a given data problem.
• Overall learn advanced Python or programming in R and Data Analysis to implement computer science algorithms.
• Thus design and Implement a Data Science Solution on Azure.
• Build AI solutions with Azure Machine Learning.
• Azure Machine Learning tools and interfaces.
• Train a machine learning model with Azure Machine Learning.
• Learn Azure Machine Learning estimators.
• In turn work with Data in Azure Machine Learning.
• Introduction to compute targets and environments.
• Orchestrate machine learning with pipelines.
• Pass data between pipeline steps.
• Furthermore deploy machine learning models with Azure Machine Learning.
• All in all Deploy models as a real-time service.
• Consume real-time inferencing service, including troubleshooting.
• In turn Automate machine learning model selection with Azure Machine Learning.
• Thus automate machine learning tasks and algorithms.
• Preprocessing and featurization.
• Thus Run automated machine learning experiments.

Course Objectives.

All in all Learn the basics of machine learning using an approachable, and well-known programming language, Python.

By the end of the Machine Learning with Python, students will have usable knowledge of the following:

• All in all gain an Introduction to Python.
• Overall Introduction to Machine Learning.
• Learn Linear, Non-linear, Simple and Multiple regression, and their applications.
• Thus Learn algorithms, such as KNN, Decision Trees, Logistic Regression and SVM.
• Learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

Course Objectives.

All in all Master Machine Learning from scratch using Javascript and TensorflowJS.

By the end of the Machine Learning with Javascript, students will have usable knowledge of the following:

• Overall Assemble machine learning algorithms from scratch.
• Learn TensorFlowJS.
• Create Applications of Tensorflow.
• Learn Gradient Descent.
• Furthermore plotting data with JavaScript.
• Not to mention Natural Binary Classification.
• Optimize your algorithms with advanced performance and memory usage profiling.
• ML best practices.

Course Objectives.

All in all Master Machine Learning from scratch using R.

By the end of the Machine Learning in R, students will have usable knowledge of the following:

• Overall Assemble machine learning algorithms from scratch.
• Learn Built-in Datasets of R.
• Create UC Irvine Machine Learning Repository.
• Learn KNN Model.
• Furthermore Machine Learning in R with caret.
• ML best practices.

Overview of Machine learning in AWS.

Overall the AWS Machine Learning Specialty is for individuals who perform a development or data science role. In turn the goal of the course is the ability to design, implement, deploy. Including maintain machine learning (ML) solutions for organizations.

Course Objectives.

By the end of the AWS Machine Learning Course, students will have usable knowledge of the following:

• Overall choose and justify the appropriate ML approach for a given organization problem.
• Be able to Identify appropriate AWS services to implement ML solutions.
• All in all Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

Overview of Data Analytics in AWS.

Overall the AWS course, you build, design and maintain analytics solutions on AWS.

Course Objectives.

By the end of the AWS Data Analytics Course, students will have usable knowledge of the following:

• Overall define AWS data analytics services and understand how they integrate with each other.
• Be able to explain AWS data analytics services. Thus to fit in the data life cycle of collection, storage, processing, and visualization.

The career prospects for AI Courses is high in demand. Artificial Intelligence is everywhere: on all platforms and devices and in all countries around the world!

• All in all an Azure Architect
• Cloud Developer.
• Web Developer.
• Furthermore a junior developer.
• Become a Cloud DevOps Engineer.
• Thus become a Azure Software Engineer.
• Networking Specialist.