Why Machine Learning Courses South Africa?

Overall JavaScript makes machine learning accessible to web and front-end developers. Thus It offers a powerful, open-source Tensorflow. In turn js library that makes it possible to define, test, and run ML models in web browsers. Thus Master Machine Learning from scratch using Javascript and TensorflowJS.

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 JavaScript for Machine Learning as a full time student at School of IT. Thus beginning your career in Data Science.

High School

All in all Learn about JavaScript, TensorFlowJS, Machine Learning 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 ML with JavaScript and TensorFlowJS 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 Master Machine Learning from scratch using Javascript and TensorflowJS.

By the end of the Machine Learning Courses South Africa, 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.

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 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.
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

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.

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.

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.

Overall the career prospects for ML Course is high in demand. Machine Learning is everywhere: on all platforms and devices and in all countries around the world!

• All in all a Machine Learning Expert
• JavaScript Developer.
• Furthermore become a Data Engineer.
• Become a Data Analysis.
• Thus become a AI Software Engineer.
• Data Scientist.