Artificial Intelligence and Deep Learning Algorithms and Applications Master Course

Artificial Intelligence and Deep Learning Algorithms and Applications Master Course OnlineA 15-Weeks Guided Online Course Sri Amit Ray Compassionate AI Lab

In this 15-week online course, you will learn and understand the main algorithms and approaches to artificial intelligence and deep learning. You will learn the techniques to improve your AI and machine learning model building skills and algorithm development skills. It includes total 15 online one-on-one classes, for real-world case studies, and hands-on practices and exercises.

The aim of the course is to:

be able to apply the algorithms for different applications and interpret the resultsbe able to build models and algorithms and adjust parametersunderstand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

The case studies share real world stories from teams who have designed AI-driven products using human-centered AI based practices. Primarily you can use R programming or Python programming for practicing the examples and projects.

 The Course Contents

The course structure varies, depending on individuals’ experiences, needs, and interests. However, there will be a total of 15 classes. The general content of the modules of the course are as follows:

Module 1: Artificial Intelligence FundamentalsHuman Intelligence vs Artificial IntelligenceHuman brain vs Artificial IntelligenceGeneral Artificial Intelligence vs Ethical Artificial IntelligenceEthical Artificial Intelligence vs Compassionate Artificial IntelligenceDifference between Artificial Intelligence, Machine Learning, and Deep LearningRecent trends of AI from robots to humanoidsMachine Learning FrameworksFuture challenges of Artificial Intelligence Module 2: Basic Machine Learning Algorithms and PracticesHypothesis TestingLinear RegressionLogistic RegressionClusteringAnalysis of Variance (ANOVA)Principal Component AnalysisNaive BayesDecision TreeRandom ForestSupport Vector MachinesK Nearest NeighborsGradient Boosting algorithms Neural NetworksEnsemble MethodsModule 3: Feature Engineering and Model BuildingData preparationMin-max Scaling, Standardization, Log Transformation, One hot Encoding, etc.

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