Artificial Intelligence

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Artificial Intelligence

The course focuses on the basic and advanced concepts of artificial intelligence such as Deep Networks, Structured Knowledge, Machine Learning, Hacking, Natural Language Processing, Artificial and Conventional Neural Network, Recurrent Neural Network, Self-Organizing Maps, Boltzmann Machines, AutoEncoders, PCA, LDA, Dimensionality Reduction, Model Selection and Boosting. Further, the artificial intelligence course will help you target the best jobs in the market in the field and not only this you can certainly increase your chances of employability by gaining more knowledge on the associated technologies such as IoT and Robotics. As a fact of the matter, Artificial Intelligence is the simulation of human intelligence by machines and it includes learning, reasoning, self-correction, speech recognition, and machine vision. Artificial intelligence is clearly the future of technology and every company is taking a step ahead to use artificial intelligence to make their products better. It is extensively used in diverse sectors such as healthcare, business, education, finance, law, and manufacturing.

Target Audience

This course is ideal for individuals having interest in Artificial Intelligence, Machine Learning, or Deep Learning. In other words, this course is meaningful for Python Developers, Robotics Engineer, and Fresh Graduates.

Prerequisites

As such, there are no technical prerequisites for artificial intelligence. However, knowledge of python and mathematical aptitude will be beneficial.

Course Objectives

Becoming an Artificial Intelligence Engineer puts you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Artificial intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn and adapt.  

Course Curriculum

Section 1 : Artificial Intelligence
An Introduction to Artificial IntelligenceHistory of Artificial IntelligenceFuture and Market Trends in Artificial IntelligenceIntelligent Agents – Perceive-Reason-Act LoopSearch and Symbolic SearchConstraint-based ReasoningSimple Adversarial Search (Game-Playing)Neural Networks and PerceptronsUnderstanding Feedforward NetworksBoltzmann Machines and AutoencodersExploring Backpropagation
Section 2 : Deep Networks and Structured Knowledge
Deep Networks/Deep LearningKnowledge-based ReasoningFirst-order Logic and TheoremRules and Rule-based ReasoningStudying Blackboard SystemsStructured Knowledge: Frames, Cyc, Conceptual DependencyDescription LogicReasoning with UncertaintyProbability & Certainty-FactorsWhat are Bayesian Networks? Understanding Sensor ProcessingNatural Language ProcessingStudying Neural ElementsConvolutional NetworksRecurrent NetworksLong Short-Term Memory (LSTM) Networks 
Section 3 : Machine Learning and Hacking
Machine learningReprise: Deep LearningSymbolic Approaches and Multiagent SystemsSocietal/Ethical ConcernsHacking and Ethical ConcernsBehaviour and HackingJob Displacement & Societal DisruptionEthics of Deadly AIsDanger of Displacement of Humanity The future of Artificial Intelligence
Section 4 : Natural Language Processing
Natural Language Processing Natural Language Processing in PythonNatural Language Processing in RStudying Deep LearningArtificial Neural NetworksANN IntuitionPlan of AttackStudying the NeuronThe Activation FunctionWorking of Neural NetworksExploring Gradient DescentStochastic Gradient DescentExploring Backpropagation
Section 5 : Artificial and Conventional Neural Network
Understanding Artificial Neural NetworkBuilding an ANNBuilding Problem DescriptionEvaluation the ANNImproving the ANNTuning the ANNConventional Neural NetworksCNN IntuitionConvolution OperationReLU LayerPooling and FlatteningFull ConnectionSoftmax and Cross-Entropy Building a CNNEvaluating the CNNImproving the CNNTuning the CNN
Section 6 : Recurrent Neural Network
Recurrent Neural NetworkRNN IntuitionThe Vanishing Gradient ProblemLSTMs and LSTM VariationsPractical IntuitionBuilding an RNNEvaluating the RNNImproving the RNNTuning the RNN
Section 7 : Self-Organizing Maps
Self-Organizing MapsSOMs Intuition Plan of AttackWorking of Self-Organizing MapsRevisiting K-MeansK-Means ClusteringReading an Advanced SOMBuilding an SOM
Section 8 : Boltzmann Machines
Energy-Based Models (EBM)Restricted Boltzmann MachineExploring Contrastive DivergenceDeep Belief NetworksDeep Boltzmann MachinesBuilding a Boltzmann MachineInstalling Ubuntu on WindowsInstalling PyTorch
Section 9 : AutoEncoders
AutoEncoders: An OverviewAutoEncoders IntuitionPlan of AttackTraining an AutoEncoderOvercomplete hidden layersSparse AutoencodersDenoising AutoencodersContractive AutoencodersStacked AutoencodersDeep Autoencoders
Section 10 : PCA, LDA, and Dimensionality Reduction
Dimensionality ReductionPrincipal Component Analysis (PCA)PCA in PythonPCA in RLinear Discriminant Analysis (LDA)LDA in PythonLDA in RKernel PCAKernel PCA in PythonKernel PCA in R
Section 11 : Model Selection and Boosting
K-Fold Cross Validation in PythonGrid Search in PythonK-Fold Cross Validation in RGrid Search in RXGBoostXGBoost in PythonXGBoost in R