Python for Data Scientist – Machine Learning

Introduction to PythonHistory and Features of PythonBasic SyntaxVariables and Data TypesOperatorsConditional, Loop & Control StatementsFunctionsRandom ModulesIntroduction to Data ScienceData ScienceData Science vs. Data ScientistFuture scope of data scientistHow to link python with DSBasic Terminologies of DSData MiningData SetsPython packagesData CollectionsData PreprocessingData VisualizationData ModellingInstallation of PackagesPandasNumpyScipySckitNLTKPython Scientific LibrariesPandasNumpyScipySckit-LearnNLTKMatplotKerasTensorflowPyTorch

Introduction to NumpyNumpyCreate Numpy arraysNumpy operationsNumpy for statistical operationsData PreprocessingHandling Missing/Fill-na/Replace ValuesDrop Column/rowLabel EncodingOne-Hot EncodingReshapingData operationsData frame creationsStatistical functions in data operationsMerging and joining data frameConcatenateData NormalizationWhat is Data Normalization?Standard ScalarMin-Max ScalarHands on standard scalarHands on min-max scalarData CollectionHow to collect input dataRead CSV DataRead JSON DataRead XLS DataRead HTML ContentsView Data

Matplotlib VisualizationPlotting with matplotlibBar chartsPie chartsScatter plotsBox plotsHistogramBubble ChartHeat mapsGraph/line graphGeographical dataSeaborn VisualizationPlotting with seabornHistogram with gridDistplotPairplotScatter plotsBox plotsLmplotsHistogramChallenge: Visualize titanic datasetFeature EngineeringFeature ExtractionFeature SelectionEDAPairplotScatter plotsBox plotsLmplotsHistogramChallenge: Visualize titanic dataset

Machine LearningAl vs ML vs DLApplication of machine learningHow do machine learnsTypes of Machine learningSupervised LearningUn-supervised LearningNaive BayesWhat is Native Bayes?Types of Naive BayesIRIS ClassificationData ProcessingTrain and create modelPerformance EstimationAnalyse and create confusion matrixSimple Linear RegressionOrdinary Least Square and Regression ErrorsData ProcessingTrain and Test ModelTest the model and Predict Y ValuesR-Squared and its ImportanceScore and Get coefficientsCalculate RMSE (Root Mean Squared Error)Plot the predictionsSimple Linear RegressionOrdinary Least Square and Regression ErrorsData ProcessingTrain and Test ModelTest the model and Predict Y ValuesR-Squared and its ImportanceScore and Get coefficientsCalculate RMSE (Root Mean Squared Error)Plot the predictionsSimple Linear RegressionOrdinary Least Square and Regression ErrorsData ProcessingTrain and Test ModelTest the model and Predict Y ValuesR-Squared and its ImportanceScore and Get coefficientsCalculate RMSE (Root Mean Squared Error)Plot the predictionsSupport Vector MachineWhat is SVM?SVM kernel typesProblem: IRIS ClassificationData ProcessingTrain and create modelPerformance EstimationAnalyse and create confusion matrix

Logistic RegressionWhat is Logistic Regression?Problem: Heart Disease PredictionBuild ModelPerformance EstimationAnalyse and create confusion matrixDecision TreeWhat is decision tree?Decision Tree ParametersProblem: IRIS ClassificationData ProcessingTrain and create modelEvaluate ModelRandom ForestWhat is random forest?Ensemble LearningBagging and Boosting ClassifiersProblem: Cardio Vascular DiseaselmplementationEvaluate ModelClassObjectsConstructorsConstructorsEncapsulationInheritancePolymorphismSuper and this KeywordAbstractionInheritanceNearest NeighborWhat is KNN?KNN parametersProblem: Cardio Vascular DiseaseData collection and preprocessinglmplementationEvaluate Model

Evaluate Classification metricsEvaluate AccuracyClassification metricsWhat is Threshold and PHAPTER Adjusting ThresholdsAUC ROC CurveDimension ReductionWhy to reduce dimensions and Importance of PCA?Steps to calculate PCAImplementation of PCAVisualizationRegressionWhat is Ridge regression?Implement Ridge RegressionPlot Ridge Regression LineLasso Regression or L1 Penaltylmplement lasso RegressionPlot lasso Regression LineOver fitting and under fittingWhat is over fitting?How to avoid over fitting?What is under fitting?How to avoid under fitting?Cross ValidationWhat is Cross Validation?How Cross Validation WorksPrepare for Cross ValidationParameter and implementation of Cross ValidationUnderstand the results of Cross Validation)Hands On - Analyse the Result

Hyper Tuning for modelWhat is Hyper parameter Tuning?Grid Search and Randomized Search ApproachGridSearchCV Parameters ExplainedCreate GirdSearchCV ObjectFit data to GridSearchCVUnderstand GridSearchCV ResultsImplementation of hypertuningGridSearchCV using Logistic RegressionGridSearchCV using Support VectorRandomized Search using random forestSelect Best ModelRandomized SearchModel Selection SummaryClusteringWhat is Clustering?How the clusters are formed?Problem Understanding: Customer SegmentationGet, Visualize and Normalize the dataImport KMeans and Understand ParametersUnderstanding KMeans++ Initialization MethodCreate ClustersVisualize and create different number of clustersUnderstand Elbow Method to Decide number of ClusterImplement Elbow Method

NLPWhat is NLPApplication of NLPRemove punctuationTokenizeRemove stop wordsStem wordsLemmatizePaddingPart of Speech TaggingName Entity RelationshipSentiment AnalysisBuild our spam detectorRead and preprocess dataNLP techniquesTokenizationTokens to vectorsNaive BayesLogistic RegressionSentiment AnalysisEvaluate MetricsRecommendation SystemWhat is Recommendation System?How Do Recommendation Works?Types of RecommendationContent Based RecommendationWhat is Content based recommendation?Advantages and drawbacksHands on Content based recommendation codeCollaborative FilteringWhat is Collaborative filtering?Advantages and drawbacksKinds of collaborative filteringHands on collaborative filtering codeHybrid Recommendation SystemWhat is hybrid recommendation?Advantages and Drawbacks

NLPWhat is NLPApplication of NLPRemove punctuationTokenizeRemove stop wordsStem wordsLemmatizePaddingPart of Speech TaggingName Entity RelationshipSentiment AnalysisBuild our spam detectorRead and preprocess dataNLP techniquesTokenizationTokens to vectorsNaive BayesLogistic RegressionSentiment AnalysisEvaluate MetricsRecommendation SystemWhat is Recommendation System?How Do Recommendation Works?Types of RecommendationContent Based RecommendationWhat is Content based recommendation?Advantages and drawbacksHands on Content based recommendation codeCollaborative FilteringWhat is Collaborative filtering?Advantages and drawbacksKinds of collaborative filteringHands on collaborative filtering codeHybrid Recommendation SystemWhat is hybrid recommendation?Advantages and Drawbacks

Deep LearningWhat is Neuron and Artificial Neural Network?How Artificial Neural Network works?What is Keras and Tensorflow?What is a Tensor in Tensorflow?Installing Keras, backend and TensorflowOptimizationKeras Model Building and StepsLayers - Overview and ParametersActivation FunctionsLayers - Softmax Activation FunctionWhat is a Loss Function?Cross Entropy Loss FunctionsOptimization - What is it?Optimization - Gradient DescentDeep Neural LayersInitializers - Vanishing and Exploding Gradient ProblemLayers - Initializers explainedProblem Understanding: Disease PredictionRead and process the dataDefine the Keras Neural Network ModelCompile the Keras Neural Network ModelEvaluate the result

Project on Application of data science Part 1Problem understanding: Loan Approval Prediction&nbspRead and preprocess dataData splittingClassification - Naïve BayesClassification – Support Vector MachineClassification - Random ForestClassification - Logistic RegressionTrain the modelEvaluate performance metricsProject on Application of data science Part 2Problem Understanding : Zomato Restaurant ReviewRead and preprocess dataNLPData SplittingClassification - Naïve Bayes (Multinomial)Classification - Support Vector MachineClassification - Random ForestClassification – Decision TreeClassification - Logistic RegressionTrain the modelEvaluate performance metricsVisualization using matplotlib and seabornProject on Application of data science Part 3Problem Understanding : Product RecommendationRead and preprocess dataCollaborative filteringContent based filteringRecommend the productEvaluate metrics

DevLustro Academy's Python course for data science is a comprehensive program designed to teach the fundamentals of Python programming and its application in data science.

There are no specific prerequisites for enrolling in the Python course for data science. However, a basic understanding of programming concepts and mathematics would be beneficial.

Yes, upon successful completion of the course, you will receive a certificate from DevLustro Academy, which can be a valuable addition to your resume.

Yes, upon successful completion of the course, you will receive a certificate from DevLustro Academy, which can be a valuable addition to your resume.

You can enroll in the course by visiting DevLustro Academy's website and following the enrollment process outlined for the Python course for data science.

To get started with machine learning in Python, you can:Learn the basics of machine learning algorithms and concepts.Explore the Scikit-learn library and its documentation.Work on hands-on projects and tutorials to apply machine learning techniques to real-world datasets.

At DevLustro Academy, you can reach out to instructors, mentors, or fellow learners for support and guidance. Additionally, online forums, community groups, and social media platforms can be valuable resources for getting help, sharing ideas, and collaborating on projects.

Python is a versatile, high-level programming language known for its simplicity and readability. It's crucial for data science at DevLustro Academy because it offers a rich ecosystem of libraries and tools specifically designed for data analysis, visualization, and machine learning.

DevLustro Academy offers various resources for learning Python for data science, including:Instructor-led training courses with experienced faculty.Self-paced online courses and tutorials.Practical workshops and projects.Access to educational materials, books, and online platforms like Coursera, Udemy, and DataCamp.