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AI and Machine LearningMaster Program bySprintzeal
AI and Machine LearningMaster Program by SprintzealCourse CurriculumProgramming BasicsModule 1 - Course IntroductionModule 2- Programming BasicsIntroduction to Artificial IntelligenceModule 1 - Decoding Artificial IntelligenceModule 2 - Fundamentals of Machine Learning and Deep LearningModule 3 - Machine Learning WorkflowModule 4 - Performance MetricsStatistics EssentialModule 1 - IntroductionModule 2 - Sample or Population Data?Module 3 - The Fundamentals of Descriptive StatisticsModule 4 - Measures of Central Tendency, Asymmetry, and VariabilityModule 5 - Practical Example: Descriptive StatisticsModule 6 - DistributionsModule 7 - Estimators and Estimates
Module 8 - Confidence Intervals: Advanced TopicsModule 9 - Practical Example: Inferential StatisticsModule 10 - Hypothesis Testing: IntroductionModule 11 - Hypothesis TestingModule 12 - Practical Example: Hypothesis TestingModule 13 - The Fundamentals of Regression AnalysisModule 14 - Subtleties of Regression AnalysisModule 15 - Assumptions for Linear Regression AnalysisModule 16 - Dealing with Categorical DataModule 17 - Practical Example: Regression AnalysisPython for Data ScienceModule 1 - Python BasicsModule 2 - Python Data StructuresModule 3 - Python Programming FundamentalsModule 4 - Working with Data in PythonModule 5 - Working with NumPy ArraysData Science with PythonModule 1 - Data Science OverviewModule 2 - Data Analytics OverviewModule 3 - Statistical Analysis and Business ApplicationsModule 4 - Python Environment Setup and EssentialsModule 5 - Mathematical Computing with Python (NumPy)Module 6 - Scientific Computing with Python (SciPy)
Module 7 - Data Manipulation with PandasModule 8 - Machine Learning with Scikit–LearnModule 9 - Natural Language Processing with Scikit LearnModule 10 - Data Visualization in Python using MatplotlibModule 11 - Web Scraping with BeautifulSoupModule 12 - Python Integration with Hadoop MapReduce and SparkMachine LearningModule 1 - Introduction to Artificial Intelligence and Machine LearningModule 2: Data PreprocessingModule 3: Supervised LearningModule 4: Feature EngineeringModule 5: Supervised Learning-ClassificationModule 6: Unsupervised LearningModule 7: Time Series ModellingModule 8: Ensemble LearningModule 9: Recommender SystemsModule 10: Text MiningDeep Learning with TensorFlow and KerasModule 1 - AI and Deep Learning IntroductionModule 2 - Artificial Neural NetworkModule 3 - Deep Neural Network and ToolsModule 4 - Deep Neural Net Optimization, Tuning, and Interpretability
Module 5 - Convolutional Neural Net (CNN)Module 6 - Recurrent Neural NetworksModule 7 - AutoencodersAdvanced Deep Learning and Computer VisionModule 1 - Course IntroductionModule 2 - Prerequisites for the courseModule 3 - RBM and DBNsModule 4 - Variational AutoEncoderModule 5 - Working with Deep Generative ModelsModule 6 - Applications: Neural Style Transfer and Object DetectionModule 7 - Distributed & Parallel Computing for Deep Learning ModelsModule 8 - Reinforcement LearningModule 9 - Deploying Deep Learning Models and BeyondNatural Language Processing and Speech RecognitionModule 1 - Introduction to Natural Language ProcessingModule 2 - Feature Engineering on Text DataModule 3 - Natural Language Understanding TechniquesModule 4 - Natural Language GenerationModule 5 - Natural Language Processing LibrariesModule 6 - Natural Language Processing with Machine Learning and Deep LearningModule 7 - Introduction of Speech Recognition
Module 8- Signal Processing and Speech Recognition ModelsModule 9 - Speech to TextModule 10 - Text to SpeechModule 11 - Voice Assistant DevicesReinforcement LearningModule 1 - Introduction to Reinforcement LearningModule 2 - Reinforcement Learning Framework and ElementsBanditModule 4 - Markov Decision ProcessModule 5 - Solution MethodsModule 6 - Q-value and Advantage Based AlgorithmsAI and Machine Learning Hands-On ProjectMaster Program CertificationModule 3 - Multi-Arm
AI and Machine Learning Master Program by Sprintzeal Course Curriculum Programming Basics Module 1 - Course Introduction . Module 12 - Python Integration with Hadoop MapReduce and Spark Machine Learning Module 1 - Introduction to Artificial Intelligence and Machine Learning Module 2: Data Preprocessing Module 3: Supervised Learning Module 4: Feature Engineering Module 5: Supervised Learning .
