Why Neural Networks Training South Africa?

Overall Artificial neural networks, usually simply called neural networks or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

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? Learn Neural Networks as a full time student at School of IT. Thus beginning your career in Artificial Intelligence.

High School

Learn about Neural Networks 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 Artificial Intelligence (AI) Training in South Africa 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.

By the end of the Applied AI from Scratch in Python Training Course, students will have usable knowledge of the following:

Supervised learning: classification and regression

• Machine Learning in Python: intro to the scikit-learn API
  • linear and logistic regression
  • support vector machine
  • neural networks
  • random forest
• Setting up an end-to-end supervised learning pipeline using scikit-learn
  • working with data files
  • imputation of missing values
  • handling categorical variables
  • visualizing data

Python frameworks for for AI applications:

• TensorFlow, Theano, Caffe and Keras
• AI at scale with Apache Spark: Mlib

Advanced neural network architectures

• convolutional neural networks for image analysis
• recurrent neural networks for time-structured data
• the long short-term memory cell

Unsupervised learning: clustering, anomaly detection

• implementing principal component analysis with scikit-learn
• implementing autoencoders in Keras

Practical

• image analysis
• forecasting complex financial series, such as stock prices,
• complex pattern recognition
• natural language processing
• recommender systems

Understand limitations of AI methods: modes of failure, costs and common difficulties

• • overfitting
  • bias/variance trade-off
  • biases in observational data
  • neural network poisoning

Course Objectives.

By the end of the Deep Reinforcement Learning with Python Training Course, students will have usable knowledge of the following:

• Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
• Apply advanced Reinforcement Learning algorithms to solve real-world problems.
• Build a Deep Learning Agent.

Course Objectives.

By the end of the Introduction to the use of neural networks Training Course, students will have usable knowledge of the following:

The Fundamentals

• Whether computers can think of?
• Imperative and declarative approach to solving problems
• Purpose Bedan on artificial intelligence
• The definition of artificial intelligence. Turing test. Other determinants
• The development of the concept of intelligent systems
• Most important achievements and directions of development

Neural Networks

• • Concept of neurons and neural networks
  • A simplified model of the brain
  • Opportunities neuron
  • XOR problem and the nature of the distribution of values
  • The polymorphic nature of the sigmoidal
  • Other functions activated
  • Construction of neural networks
  • Concept of neurons connect
  • Neural network as nodes
  • Building a network
  • Neurons
  • Layers
  • Scales
  • Input and output data
  • Range 0 to 1
  • Normalization
  • Learning Neural Networks
  • Backward Propagation
  • Steps propagation
  • Network training algorithms
  • range of application
  • Estimation
  • Problems with the possibility of approximation by
  • Examples
  • XOR problem
  • Lotto?
  • Equities
  • OCR and image pattern recognition
  • Other applications
  • Implementing a neural network modeling job predicting stock prices of listed

Course Objectives.

By the end of the Neural Network in R Training Course, students will have usable knowledge of the following:

Introduction to Neural Networks

1. What are Neural Networks
2. What is current status in applying neural networks
3. Neural Networks vs regression models
4. Supervised and Unsupervised learning

Overview of packages

1. nnet, neuralnet and others
2. Differences between packages and itls limitations
3. Visualizing neural networks

Applying Neural Networks

• Concept of neurons and neural networks
• A simplified model of the brain
• Opportunities neuron
• XOR problem and the nature of the distribution of values
• The polymorphic nature of the sigmoidal
• Other functions activated
• Construction of neural networks
• Concept of neurons connect
• Neural network as nodes
• Building a network
• Neurons
• Layers
• Scales
• Input and output data
• Range 0 to 1
• Normalization
• Learning Neural Networks
• Backward Propagation
• Steps propagation
• Network training algorithms
• range of application
• Estimation
• Problems with the possibility of approximation by
• Examples
• OCR and image pattern recognition
• Other applications
• Implementing a neural network modeling job predicting stock prices of listed

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

• All in all AI Architect
• Cloud Developer.
• Data Analyst.
• Furthermore a Data Scientist.
• Become a Cloud DevOps Engineer.
• Thus become a Azure Software Engineer.
• Networking Specialist.