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Machine Learning and NLP Basics · LearnSpace
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Machine Learning and NLP Basics

Курс от Edureka
Начальный≈ 19.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

The Machine Learning and NLP Basics course is a learning resource designed for individuals interested in developing foundational knowledge of machine learning (ML) and natural language processing (NLP). This course is ideal for students, data scientists, software engineers, and anyone seeking to build or strengthen their skills in machine learning and natural language processing. Whether you are starting your journey or seeking to reinforce your foundation, this course provides practical skills and real-world applications. Throughout this course, participants will gain a solid understanding of machine learning fundamentals, explore various ML types, work with classification and regression techniques, and engage in practical assessments. By the end of this course, you will be able to: - Understand and apply core concepts of machine learning and NLP. - Differentiate between various types of machine learning and when to use them. - Implement classification, regression, and optimization techniques in ML. - Utilize deep learning models for complex problem-solving. - Navigate TensorFlow for building and training models. - Explore CNNs and RNNs for image and sequence data processing. - Explore NLP techniques for text analysis and classification. Learners are expected to have a basic understanding of programming. Familiarity with Python and AI fundamentals is helpful but not required. It is designed to equip learners with the skills and confidence necessary to navigate the evolving landscape of AI and data science, laying a strong foundation for further learning and professional growth.

Навыки, которые вы освоите

Machine LearningNatural Language ProcessingSupervised LearningArtificial IntelligenceTensorflowText MiningDeep LearningMachine Learning AlgorithmsMachine Learning MethodsArtificial Neural NetworksClassification AlgorithmsModel TrainingData ScienceData PreprocessingPredictive ModelingConvolutional Neural NetworksModel OptimizationRecurrent Neural Networks (RNNs)Applied Machine Learning

Программа курса

4 модулей · 182 учебных материалов

01Machine Learning38 материалов

Machine Learning Fundamentals

Course IntroductionВидеоCourse OverviewЧтениеArtificial Intelligence EssentialsВидеоDisciplines of AIВидео

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Edureka

Преподаватель курса

 Machine Learning and NLP Basics
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Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 19.5 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Various Application of AI DisciplinesВидео
Types of AIВидео
Type-I of Artificial IntelligenceВидео
Type-II of Artificial IntelligenceВидео
Machine Learning FundamentalsВидео
Applications of Machine LearningВидео
Predictive ML ModelsВидео
Classification and Other ModelsВидео
How to use Discussion Forums?Чтение
Relationship between Artificial Intelligence and Machine LearningОбсуждение
Knowledge Check: Machine Learning FundamentalsЗадание
Do You Already Know About Machine Learning and NLP?DIALOGUE

Machine Learning Types

ML Algorithms: Deep DiveВидеоML Algorithms - Part llВидеоSupervised Machine LearningВидеоApplications of Supervised LearningВидеоMarket Segement Strategies of Unsupervised Machine LearningВидеоIntroduction to Unsupervised Machine LearningВидеоSemi-supervised LearningВидеоReinforcement LearningВидеоUse - Case of ReinforcementВидеоKnowledge Check: Machine Learning TypesЗадание

Classification and Regression

ClassificationВидеоTypes of Classification Algorithm ВидеоOther types of Classification AlgorithmВидеоDemonstration on ClassificationВидеоFeature Scailing and Training the ClassifierВидеоVisualization of Classification ReportВидеоRegressionВидеоDemonstration on Regression ВидеоMachine Learning Case Study: Predictive Modeling for Early Detection of DiabetesЧтениеKnowledge Check: Classification and RegressionЗадание

Module Wrap Up and Assessments

Module Summary: Machine LearningЧтениеKnowledge Check: Machine LearningЗадание
02Deep Learning90 материалов

Deep Learning - Overview

Deep Learning FundamentalsВидеоMachine Learning Vs. Deep LearningВидеоHuman Brain vs Neural NetworkВидеоIntroduction to Neural NetworkВидеоCurse of DimensionalityЧтениеPerceptronВидеоComponents of PerceptronВидеоLearning RateВидеоLower Learning RateВидеоEpochВидеоImportance of EpochВидеоBatch SizeВидеоChoosing the Right Batch SizeВидеоSingle Layer PerceptronВидеоWorking of Single Layer PerceptronВидеоImpact of Activation function on Single-layer PerceptronsОбсуждениеKnowledge Check: Deep Learning - OverviewЗадание

Tensorflow

Introduction to TensorFlow ЧтениеInstalling TensorFlow ВидеоTensorFlow InstallationВидеоDefining Sequence model layersВидеоActivation FunctionВидеоAdvanced Activation FunctionsВидеоLayer Types

Digit Classification using Simple Neural Network

Digit Classification using Simple Neural Network in TensorFlow 2.xВидеоImproving the modelВидеоAdding Hidden LayerВидеоHidden Layers in Neural NetworkВидеоAdding DropoutВидеоAdam OptimizerВидео

Convolutional Neural Networks

Image Classification ExampleВидеоImage Classification - IIВидеоConvolution: A Detailed ExplanationЧтениеConvolutional Neural NetworkВидеоWhy is CNN Preferred over MLPВидеоConvolution Layer: In-Depth ExplorationЧтение

Recurrent Neural Network and Long Short-Term Memory

RNN FundamentalsЧтениеArchitecture of RNN ЧтениеImplementing RNNВидеоLSTM BasicsВидеоLSTM StructureВидеоGateВидеоGates in LSTM Видео

Module Wrap - up and Assessment

Module Summary: Deep LearningЧтениеKnowledge check: Deep LearningЗадание
03Natural Language Process50 материалов

Introduction to Text Mining

Text MiningВидеоNeed of Text MiningВидеоApplications of Text MiningВидеоComparison of Applications in Text MiningВидеоSetting Up NLTKВидеоDemonstration on Setting-up NLTKВидеоAccessing the NLTK CorporaВидеоNatural Language Processing (NLP) TutorialЧтениеEnhance the effectiveness of Text-Based ApplicationsОбсуждениеKnowledge Check: Introduction to Text Mining Задание

Extracting, Cleaning and Preprocessing Text

TokenizationВидеоTypes of TokenizationВидеоUses of TokenizationВидеоFrequency distribution in NLP ЧтениеDetailed Exploration on Tokenizers and its TypesЧтениеBigrams, Trigrams & NgramsВидео

Text Classification

Bag of WordsВидеоDemonstration on Bag of Words ApproachВидеоDemonstration on Bag of Words Approach - IIВидеоText ProcessingВидеоCount VectorizerВидеоCount Vectorization in Scikit - LearnВидео

Module Wrap-up and Assessment

Module Summary: Natural Language ProcessЧтениеKnowledge Check: Natural Language ProcessЗадание
04Course Wrap-up and Assessments4 материалов
Practice Project: Developing an AI-Powered System for Fraud Detection in Online TransactionsЧтениеMachine Learning and NLP Foundations: A Scenario-Based ExerciseDIALOGUEEnd Course Knowledge CheckЗаданиеCourse SummaryВидео
Видео
Types of Layer TypeВидео
Model CompilationВидео
Uses of Model Compilation Видео
Model OptimizerВидео
Understanding Model OptimizerВидео
Uses of Model OptimizerВидео
Impact of Activation function on Neural Network PerformanceОбсуждение
Knowledge Check: TensorflowЗадание
How to use Adam Optimizer?Видео
Neural Network Model for Digit ClassificationОбсуждение
Knowledge Check: Digit Classification using Simple Neural NetworkЗадание
ReLU LayerВидео
PoolingВидео
Implementation of ReLU LayerВидео
Data FlatteningВидео
Stacking up the LayersВидео
Flattening LayerВидео
Fully Connected LayerВидео
The Final LayerВидео
Predicting a cat or a dogВидео
Model Building For Cat Vs. Dog ClassificationВидео
Demonstration on Dog Vs Cat - IВидео
Demonstration on Dog Vs Cat - IIВидео
Demonstration on Dog Vs Cat - IIIВидео
Importance Of Saving And Loading A ModelВидео
Saving and Loading a ModelВидео
Demo-Saving and Loading the ModelВидео
Significance of Fully Connected LayersОбсуждение
Knowledge Check: Convolutional Neural NetworksЗадание
Input, Output and Forget GateВидео
LSTM ArchitectureВидео
LSTM Architecture: OverviewВидео
LSTM Architecture: GATESВидео
Importance of LSTM ArchitectureВидео
Sequence Based ModelВидео
Sequence Based Model in CNNВидео
Sequence Based Model in CNN: Continuation Видео
Types of LSTMВидео
Vanilla LSTM and Stacked LSTMВидео
Convolutional Neural Network LSTMВидео
Bi-Directional LSTMВидео
How to increase the Efficiency of the Model?Чтение
Backpropagation through TimeЧтение
Differences between traditional RNNs and LSTM networksОбсуждение
Knowledge Check: Recurrent Neural Network and Long Short-Term MemoryЗадание
Demonstration on Bigrams, Trigrams and NgramsВидео
StemmingВидео
Different types of StemmerВидео
Demonstration on StemmingВидео
LemmatizationВидео
Lemmatization Using NLTKВидео
StopwordsВидео
Demonstration on StopwordsВидео
POS TaggingВидео
Common Tags and Descriptions of POS Видео
Need of POS TagsВидео
Demonstration on Parts of SpeechВидео
Advantages and Limitations of NLP TechniquesОбсуждение
Knowledge Check: Extracting, Cleaning and Preprocessing TextЗадание
Term Frequency (TF)Видео
Term frequency in Scikit - LearnВидео
Demonstration on Term Frequency Видео
Demonstration on Term Frequency - IIВидео
Inverse Document Frequency (IDF)Видео
Inverse Document Frequency (IDF) ExampleВидео
Multinomial Naive Bayes ClassifierВидео
Multinomial Naive Bayes AlgorithmВидео
Leveraging Confusion MatrixВидео
Representation of Confusion MatrixВидео
Converting Text Data into Features and LabelsОбсуждение
Knowledge Check: Text ClassificationЗадание