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Advanced Machine Learning, Big Data, and Deep Learning · LearnSpace
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Advanced Machine Learning, Big Data, and Deep Learning

Курс от Packt
Продвинутый≈ 12 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive deep into advanced machine learning techniques, including data mining, dimensionality reduction, reinforcement learning, and deep learning. You'll gain hands-on experience with tools like K-Nearest Neighbors, Principal Component Analysis, and Apache Spark while working with real-world datasets. The course emphasizes key machine learning concepts such as model evaluation, cross-validation, and handling unbalanced data. As you progress, you'll explore advanced neural networks like Convolutional and Recurrent Neural Networks, with practical applications such as sentiment analysis and handwriting recognition. Learn how to deploy models, use transfer learning, and understand the ethics behind machine learning and deep learning. This course is ideal for anyone with a basic understanding of machine learning who wants to advance their skills with real-world applications and big data tools. Gain the expertise needed to work with cutting-edge technologies in machine learning and deep learning. Ideal for data scientists, machine learning engineers, and anyone with a keen interest in AI and its real-world applications.

Навыки, которые вы освоите

Deep LearningTransfer LearningKeras (Neural Network Library)Convolutional Neural NetworksTensorflowRecurrent Neural Networks (RNNs)Apache SparkArtificial Neural NetworksData PreprocessingA/B TestingDimensionality ReductionData CleansingMachine LearningMachine Learning SoftwareModel DeploymentModel EvaluationResponsible AIMachine Learning AlgorithmsModel TrainingData Processing

Программа курса

5 модулей · 67 учебных материалов

01More Data Mining and Machine Learning Techniques13 материалов

More Data Mining and Machine Learning Techniques

Introduction to the Course 'Advanced Machine Learning, Big Data, and Deep Learning'ЧтениеFull Specialization ResourceЧтениеK-Nearest-Neighbors: ConceptsВидео[Activity] Using KNN to Predict a Rating for a MovieВидео

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Packt - Course Instructors

Преподаватель курса

Advanced Machine Learning, Big Data, and Deep Learning
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Обучение на Coursera

≈ 12 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Dimensionality Reduction; Principal Component Analysis (PCA)Видео
[Activity] PCA Example with the Iris DatasetВидео
Data Warehousing Overview: ETL and ELTВидео
Reinforcement LearningВидео
[Activity] Reinforcement Learning and Q-Learning with GymВидео
Understanding a Confusion MatrixВидео
Measuring Classifiers (Precision, Recall, F1, ROC, AUC)Видео
Introduction to K-Nearest NeighborsDIALOGUE
More Data Mining and Machine Learning Techniques - AssessmentЗадание
02Dealing with Real-World Data12 материалов

Dealing with Real-World Data

Bias/Variance TradeoffВидео[Activity] K-Fold Cross-Validation to Avoid OverfittingВидеоData Cleaning and NormalizationВидео[Activity] Cleaning Web Log DataВидеоNormalizing Numerical DataВидео[Activity] Detecting OutliersВидеоFeature Engineering and the Curse of DimensionalityВидеоImputation Techniques for Missing DataВидеоHandling Unbalanced Data: Oversampling, Undersampling, and SMOTEВидеоBinning, Transforming, Encoding, Scaling, and ShufflingВидеоUnderstanding the Bias/Variance TradeoffDIALOGUEDealing with Real-World Data - AssessmentЗадание
03Apache Spark: Machine Learning on Big Data12 материалов

Apache Spark: Machine Learning on Big Data

[Activity] Installing Spark - Part 1Видео[Activity] Installing Spark - Part 2ВидеоSpark IntroductionВидеоSpark and the Resilient Distributed Dataset (RDD)ВидеоIntroducing MLLibВидеоIntroduction to Decision Trees in SparkВидео[Activity] K-Means Clustering in SparkВидеоTF / IDFВидео[Activity] Searching Wikipedia with SparkВидео[Activity] Using the Spark DataFrame API for MLLibВидеоSetting Up Apache SparkDIALOGUEApache Spark: Machine Learning on Big Data - AssessmentЗадание
04Experimental Design / ML in the Real World8 материалов

Experimental Design / ML in the Real World

Deploying Models to Real-Time SystemsВидеоA/B Testing ConceptsВидеоT-Tests and P-ValuesВидео[Activity] Hands-On with T-TestsВидеоDetermining How Long to Run an ExperimentВидеоA/B Test GotchasВидеоDeploying Machine Learning Models at ScaleDIALOGUEExperimental Design / ML in the Real World - AssessmentЗадание
05Deep Learning and Neural Networks22 материалов

Deep Learning and Neural Networks

Deep Learning PrerequisitesВидеоThe History of Artificial Neural NetworksВидео[Activity] Deep Learning in the TensorFlow PlaygroundВидеоDeep Learning DetailsВидеоIntroducing TensorFlowВидео[Activity] Using TensorFlow, Part 1Видео[Activity] Using TensorFlow, Part 2Видео[Activity] Introducing KerasВидео[Activity] Using Keras to Predict Political AffiliationsВидеоConvolutional Neural Networks (CNNs)Видео[Activity] Using CNNs for Handwriting RecognitionВидеоRecurrent Neural Networks (RNNs)Видео[Activity] Using a RNN for Sentiment AnalysisВидео[Activity] Transfer LearningВидеоTuning Neural Networks: Learning Rate and Batch Size HyperparametersВидеоDeep Learning Regularization with Dropout and Early StoppingВидеоThe Ethics of Deep LearningВидеоUnderstanding Deep Learning: Neural Networks, CNN, and RNNDIALOGUEConclusion to the Course 'Advanced Machine Learning, Big Data, and Deep Learning'ЧтениеDeep Learning and Neural Networks - AssessmentЗаданиеFull course practice assessmentЗаданиеFull course assessmentЗадание