Complete Data Science,Machine Learning,DL,NLP Bootcamp
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Getting Started
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Python Programming Language
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Python Control Flow
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Inbuilt Data Structures In Python
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Function In Python
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Function Practice Question
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Inbuilt Data Structure – Practice QuestionSum of List ElementsLargest Element in a ListRemove Duplicate in a ListCheck if all elements in a List are UniqueReverse a ListNumber of Odd and Even Elements in a listCheck if List is Subset of Another ListMaximum Difference between 2 consecutive elements in a ListMerge two sorted ListRotate a ListMerge 2 list into DictionaryMerge Multiple DictionaryWords Frequency in a SentencePalindromic TupleMerge Dictionaries with Common Keys
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Importing Creating Modules And Packages
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File handling In Python
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Exception Handling In Python
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OOPS Concepts With Classes And ObjectsClasses And Objects In PythonClasses And Objects Practise Questions And SolutionsInheritance In OOPSPolymorphism In OOPSEncapsulation In OOPSAbstraction In OOPSPractise Assignments With SolutionsMagic Methods In PythonOperator Overloading In PythonCustom Exception HandlingComplete OOPS Practise Question With Solutions
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Advance Python
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Data Analysis With Python
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Working With Sqlite3
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Logging In Python
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Python Multi Threading and Multi Processing
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Memory Management With Python
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Getting Started With Flask Framework
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Getting Started With Streamlit Framework
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Getting Started With StatisticsWhat is Statistics And its ApplicationTypes Of StatisticsPopulation Vs Sample DataMeasure Of Central TendencyMeasure Of DispersionWhy Sample Variance Is Divided By n-1?Standard DeviationWhat Are Variables?What are Random VariablesHistograms- Descriptive StatisticsPercentile And Quartiles- Descriptive Statistics5 Number Summary-Descriptive StatisticsCorrelation And Covariance
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Introduction To Probability
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Probability Distribution Function For DataThe Relationship Between PDF,PMF And CDFTypes Of Probability DistributionBernoulli DistributionBinomial DistributionPoisson DistributionNormal/ Gaussian DistributionStandard Normal Distribution And Z ScoreUniform DistributionLog Normal DistributionPower Law DistributionPareto DistributionCentral Limit TheoremEstimates
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Inferential StatisticsHypothesis Testing And MechanismWhat is P value?Z Test- Hypothesis TestingStudent t DistributionT Stats With T test Hypothesis TestingZ test Vs T testType 1 And Type 2 ErrorBayes TheoremConfidence Interval And MArgin Of ErrorWhat is Chi Square TestChiSquare Goodness OF FitAnnova TestAssumptions Of AnnovaTypes Of AnnovaPartioning Of Variance In Annova
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Feature Engineering
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Exploratory Data Analysis and Feature Engineering
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Introduction To Machine Learning
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Understanding Complete Linear Regression Indepth Intuition And PracticalsSimple Linear Regression IntroductionUnderstanding Simple Linear regression EquationsCost FunctionConvergence AlgorithmConvergence Algorithm Part02Multiple Linear regressionPerformance MetricsMSE, MAE, RMSEOverfitting and UnderfittingLinear Regression with OLSSimple Linear Regression PracticalMultiple Linear regressionPolynomial Regression IntuitionPolynomial Regression ImplementationPipeline in Polynomial
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Ridge, Lasso And ElasticNet ML ALgorithms
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Steps By Step Project Implementation With LifeCycle OF ML ProjectBasic Simple Linear Regression ProjectMultiple Linear Regression Projects With AssumptionsBasic Regression Project From Scratch-EDA And Feature EngineeringModel Training With Cross Validation Using Lasso RegressionModel Training With Ridge and Elastic net With Cross ValidationModel Pickling In ML ProjectEnd To End ML Project ImplementationProject Deployment In AWS
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Logistic Regression
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Support Vector Machines
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Naive Baye’s Theorem
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K Nearest Neighbour ML Algorithm
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Decision Tree Classifier And Regressor
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Random Forest Machine Learning
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Adaboost Machine Learning Algorithm
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Gradient Boosting
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Xgboost Machine Learning Algorithms
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Unsupervised Machine Learning
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PCA
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K means Clustering Unsupervised ML
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Hierarichal Clustering
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DBSCAN Clustering
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Silhoutte Clustering
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Anomaly Detection Machine Learning Algorithms
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Dockers For Beginners
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GIT For Beginners
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End To End Machine Learning Project With AWS, Azure DeploymentEnd To End ML Project With Deployment-Github And Code Set UpImplementing Project Structure, Logging And Exception HandlingDiscussing Project Problem Statement,EDA And Model TrainingData Ingestion ImplementationData Transformation Using Pipelines ImplementationModel Trainer ImplementationModel Hyperparameter Tuning ImplementationBuilding Prediction PipelineML Project Deployment Using AWS BeanstalkDeployment EC2 Instance With ECRDeployment Azure With Container And Images
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End To End MLOPS Projects With ETL Pipelines- Building Network Security SystemProject Structure Set up With EnvironmentGithub Repository Set Up With VS CodePackaging the Project With Setup.pyLogging And Exception Handling ImplementationIntroduction To ETL PipelinesSetting Up MongoDb AtlasETL Pipeline Setup With PythonData Ingestion ArchitectureImplementing Data Ingestion ConfigurationImplementing Data Ingestions ComponentImplementing Data Validation-Part 1Implementing Data Validation-Part 2Data Transformation ArchitectureData Transformation ImplementationModel Trainer Implementation- Part 1Model Trainer And Evaluation With Hyperparameter TuningModel Experiment Tracking With MLFLOWMLFLOW Experiment Tracking With Remote Respository DagshubModel Pusher ImplementationModel Training Pipeline ImplementationBatch Prediction Pipeline ImplementationFinal Model And Artifacts Pusher To AWS S3 bucketsBuilding Docker Image And Github ActionsGithub Action-Docker Image Push to AWS ECR Repo ImplementationFinal Deployment To EC2 instance
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MLFlow Dagshub and BentoML-Complete ML Project Lifecycle
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NLP for Machine LearningRoadmap to Learn NLP for Machine LearningPractical Use cases of NLPTokenisation and Basic TerminologiesTokenisation PracticalsText Preprocessing Stemming using NLTKText Preprocessing Lemmatization NLTKText Preprocessing StopwordsParts of Speech Tagging Using NLTKNamed Entity RecognitionWhat’s Next?One Hot Encoding IntuitionAdvantages and Disadvantages of OHEBag of Words IntuitionAdvantages and Disadvantages BOWBOW implementation using NLTKN GramsN Gram BOW Implementation Using NLTKTF-IDF InstituionAdvantages and Disadvantages of TF-IDFTFIDF Practical implementation PythonWord EmbeddingsWord2Vec IntuitionWord2Vec Cbow IntuitionSkipGram Indepth IntuitionAdvantages of Word2VecAvgWord2vec Indepth IntuitionAvgWord2vec Indepth IntuitionWord2vec Practical Implementation GensimSpam ham Project using BOWSpam And Ham Project Using TFidfBest Practises For Solving ML ProblemsPart 1-Text Classification With Word2vec And AvgWord2vecPart 2- Text Classification With Word2vec And AvgWord2vecPart 1-Kindle Review Sentiment AnalysisPart 2- Kindle Review Sentiment Analysis
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Deep LearningIntroductionWhy Deep Learning is getting Popular?3 – Perception IntuitionAdvantages and Disadvantages of PerceptronANN Intuition and LearningBack Propogation and Weight UpdationChain Rule of DerivativesVanishing Gradient Problem and SigmoidSigmoid Activation FunctionSigmoid Activation Function 2.0Tanh Activation FunctionRelu activation FunctionLeaky Relu and Parametric ReluELU Activation FunctionSoftmax For Multiclass ClassificationWhich Activation Function To Apply When?Loss Function Vs Cost FunctionRegression Cost FunctionLoss Function Classification ProblemWhich Loss Function To Use When?Gradient Descent OptimisersSGDMini Batch With SGDSGD With MomentumAdagardRMSPROPAdam OptimiserExploding Gradient ProblemWeight Initialisation TechniquesDropout LayersCNN IntroductionHuman Brain Vs CNNAll you need to Know about ImagesConvolution Operation In CNNPadding In CNNOperation Of CNN Vs ANNMax, Min and Average PoolingFlattening and Fully Connected LayersCNN example with RGB
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End to End Deep Learning Project Using ANNDiscussing Classification Problem Statement And Setting Up Vs CodeFeature Transformation Using Sklearn With ANNStep By Step Training With ANN With Optimizer and Loss FunctionsPrediction With Trained ANN ModelIntegrating ANN Model With Streamlit Web APPDeploying Streamlit web app with ANN ModelANN Regresiion Practical ImplementationFinding Optimal Hidden Layers And Hidden Neurons In ANN
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NLP With Deep Learning
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Simple RNN Indepth Intuition
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End To End Deep Learning Project With Simple RNNProblem StatementGetting Started With Word Embedding LayersImplementing Word Embedding With Keras TensorflowLoading And Understanding IMDB Dataset And Feature EngineeringTraining Simple RNN With Embedding LayerPrediction From Trained Simple RNNEnd To End Streamlit Web App Integrated With RNN And deployment
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LSTM And GRU RNN Indepth Intuition
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LSTM And GRU End To End Deep Learning Project – Predicting Next Word
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Bidirectional RNN Architecture And Indepth Intuition
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Encoder Decoder | Sequence To Sequence Architecture
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Attention Mechanism- Seq2Seq Networks
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TransformersPlan Of ActionWhat And Why To Use TransformersUnderstanding the basic architecture of transformersSelf Attention Layer WorkingMulti Head AttentionFeed Forward Neural Network With Multi Head AttentionPositional Encoding Indepth IntuitionLayer NormalizationLayer Normalization ExamplesComplete Encoder transformer architectureDecoder Transformer- Plan Of ActionDecoder Transformer- Masked Multi Head Attention WorkingEncoder Decoder Multi Head AttentionFinal Decoder Linear And Softmax Layer
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Role Play
Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.
What You’ll Learn:
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Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.
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Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.
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Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.
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Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.
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Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and scikit-learn. Learn how to use these tools to build and deploy models.
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Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.
Who Is This Course For:
This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you’re a student, a professional looking to upskill, or someone looking to switch careers, this course will provide you with the knowledge and skills you need to succeed in the field of ML and NLP.
Why Take This Course:
By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.
Join us on this journey to master Machine Learning and Natural Language Processing. Enroll now and start building your future in AI.
What's included
- 99 hours on-demand video
- 19 articles
- 165 downloadable resources
- Access on mobile and TV
- Certificate of completion