Apr 26 2024 Complete Machine Learning Course With Python BaDshaH LEARNING / e-learning - Tutorials 13:15 0 Published 4/2024MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHzLanguage: English | Size: 8.35 GB | Duration: 11h 36mLearn to create Machine Learning Algorithms in Python using Different Datasets What you'll learnAround 15+ Machine learning algorithms explanation with different datasets and 15+ assignment for practiceSupervised and Unsupervised learning models,PRINCIPLE COMPONENT ANALYSIS(PCA)Solve any problem in your business, job or personal life with powerful Machine Learning modelsTrain machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & moreRequirementsBasic Python programming knowledge is necessaryGood understanding of linear algebra,StasticsDescriptionThis course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);Gain complete machine learning tool sets to tackle most real world problemsUnderstand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix,etc. and when to use them.Combine multiple models with by bagging, boosting or stackingMake use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your dataDevelop in Spyder and various IDECommunicate visually and effectively with Matplotlib and SeabornEngineer new features to improve algorithm predictionsMake use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen dataUse SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionAnd much much more!No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!Take this course and become a machine learning engineer!OverviewSection 1: IntroductionLecture 1 What Is Machine learningLecture 2 Key Skills needed to learn Machine learningLecture 3 Supervised learning vs Unsupervised LearningLecture 4 Dependent Variable vs Independent VariableLecture 5 What Does This Course CoverLecture 6 Basic Python ConceptsSection 2: Introduction to Machine Learning and Anaconda InstallationLecture 7 Introduction to Machine LearningLecture 8 Anconda InstallationSection 3: Exploratory Data AnalysisLecture 9 What is Exploratory Data Analysis(EDA)Lecture 10 knowing initial details of datasetLecture 11 Modifying or removing unwanted dataLecture 12 Retrieving DataLecture 13 Statistical InformationLecture 14 Drawing GraphsLecture 15 EDA AssignmentSection 4: OutliersLecture 16 What is OutliersLecture 17 Finding the OutliersLecture 18 IQR and handling the outliersSection 5: Simple Linear RegressionLecture 19 What is RegressionLecture 20 What is simple liner regression modelLecture 21 What is r-squared ValueLecture 22 Simple linear regression Program-1Lecture 23 Simple linear regression Program-2(train and test data)Section 6: Multiple Linear RegressionLecture 24 What is Multiple Linear RegressionLecture 25 Multiple Linear Regression -program 1Section 7: One Hot EncodingLecture 26 What Is One Hot EncodingLecture 27 One Hot Encoding-First wayLecture 28 One Hot Encoding-Second wayLecture 29 One Hot Encoding-Program 1Lecture 30 One Hot Encoding-Program 2(Third way)Section 8: Polynomial Linear RegressionLecture 31 What is Polynomial Linear RegressionLecture 32 Polynomial Linear Regression Program-1Section 9: Ridge RegressionLecture 33 What is Bias and VarianceLecture 34 What is RegularizationLecture 35 Ridge Regression-Program 1Lecture 36 Ridge Regression-AssignmentSection 10: Lasso RegressionLecture 37 What is Lasso regression and practice program-1Section 11: ElasticNet RegressionLecture 38 what is ElasticNet Regression and practice program-1Section 12: Logistic RegressionLecture 39 What is Logistic Regression and program-1Section 13: Support Vector Machine(SVM)Lecture 40 What is Support Vector MachineSection 14: Naive Bayes ClassificationLecture 41 What is Naive Bayes ClassificationLecture 42 Naive Bayes Classification Program-1Lecture 43 Naive Bayes Classification Program-2Section 15: KNN ClassifierLecture 44 KNN Classifer defination and its practice program-1Section 16: Decision TreesLecture 45 Decision Trees Defination and its program-1Section 17: Random ForestLecture 46 Random Forest Defination and its practice program-1Section 18: K-Means Clustering(unsupervised model)Lecture 47 What is K-Means ClusteringLecture 48 K-Means Clustering Program-1Section 19: Apriori AlgorithmLecture 49 What is Apriori AlgorithmSection 20: Principle Component Analysis(PCA)Lecture 50 what is Principle Component Analysis(PCA)Lecture 51 Principle Component Analysis Program-1Lecture 52 Principle Component Analysis Program-2Lecture 53 Principle Component Analysis-AssignmentSection 21: K-Fold Cross ValidationLecture 54 What is K-Fold Cross ValidationLecture 55 K-Fold Cross Validation Program-1Section 22: Model SelectionLecture 56 What is Model SelectionLecture 57 Model Selection Program-1Section 23: Assignment SolutionsLecture 58 Assignment SolutionsAnyone willing and interested to learn machine learning algorithm with Python,Anyone who want to choose carrer in Datascience,AI,Machine learning,Data analytics,Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithmsHomepagehttps://www.udemy.com/course/complete-machine-learning-course-with-python/https://rapidgator.net/file/30876dc67fe9364bad62423fe6279c99https://rapidgator.net/file/5c4e011940c2bca41bd8270daf4d8414https://rapidgator.net/file/ef38726a9e7d3fd306d294818fbedaa4https://rapidgator.net/file/e1e7c14f49bee237cef16987721ba29dhttps://rapidgator.net/file/271676c80b7b99d09b09dd908c67c7b8https://rapidgator.net/file/6a546020bc2919c887ed4002c9c385a1https://rapidgator.net/file/e3410873c957ca05d3e1f4a36147f56bhttps://rapidgator.net/file/29d29fe0cc4bf47b24c44881238b0f33https://rapidgator.net/file/ff07752097065e02489d1210876cf51fhttps://ddownload.com/xjvr3njg8u34https://ddownload.com/nuejt99em6u4https://ddownload.com/r9qnhzuephcihttps://ddownload.com/cbt74uj7vdthhttps://ddownload.com/sd16dz1r2mughttps://ddownload.com/0tt97215t80dhttps://ddownload.com/g9pe6ph3ju67https://ddownload.com/38yovj4l3ni4https://ddownload.com/eitiz3yeuix0 Related News Udemy - 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