• views: 100
• date: 19 March 2023
• posted by: voska89

Free Download What you'll learn
Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning
Requirements
simple programming knowledge is added advantage
Description
The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.The course contents are given below:Introduction to Machine LearningIntroductions to Deep LearningInstallationsUnsupervised LearningClustering, AssociationAgglomerative, Hands-on(PCA: Principal Component Analysis)DBSCAN, Hands-onMean Shift, Hands-onK Means, Hands-onAssociation Rules, Hands-onSupervised LearningRegression, ClassificationTrain Test Split, Hands-onk Nearest Neighbors, Hands-onkNN Algo ImplementationSupport Vector Machine (SVM), Hands-onSupport Vector Regression (SVR), Hands-onSVM (non linear svm params), Hands-onSVM kernel trick, Hands-onSVM mathematicsLinear Regression, Hands-onGradient Descent overviewOne Hot Encoding (Dummy vars)One Hot Encoding with Linear Regr, Hands-onNaive Bayes OverviewBayes' Concept , Hands-onNaive Bayes' Classifier, Hands-onLogistic Regression OverviewBinary Classification Logistic RegressionMulticlass Classification Logistic RegressionDecision TreeID3 Algorithm - ClassifierID3 Algorithm - RegressionInfo about Datasets
Overview
Section 1: Introduction
Lecture 1 Machine Learning Course Contents
Lecture 2 Contents update
Lecture 3 Machine Learning Introduction
Lecture 4 Deep Learning Introduction
Lecture 5 Prerequisite-Installations
Section 2: Python & NumPy
Lecture 6 Python contents
Lecture 7 Development Environment and Installation
Lecture 8 Variables and Numbers in Python (with Practical)
Lecture 9 Strings in Python (with Practical)
Lecture 10 Lists in Python (with Practical)
Lecture 11 Conditional Execution (with Practical)
Lecture 12 Loops (with Practical)
Lecture 13 Functions (with Practical)
Lecture 14 Dictionaries in Python (with Practical)
Lecture 15 Tuples in Python (with Practical)
Lecture 16 Exceptions and it's Handling
Lecture 17 Exceptions and it's Handling (with Practical)
Lecture 18 Iterators (with Strings, List, Dictionary, Tuple)
Lecture 19 Iterators Practical (with Strings, List, Dictionary, Tuple)
Lecture 20 File Support (with Practical) - part 1
Lecture 21 File Support (with Practical) - part 2
Lecture 22 JSON support (with Practical)
Lecture 23 NumPy with Practical (part 1)
Lecture 24 NumPy with Practical (part 2)
Section 3: UnSupervised Machine Learning
Lecture 25 Unsupervised Machine Learning - Overview
Lecture 26 Hierarchical Clustering : Agglomerative Clustering
Lecture 27 Agglomerative Clustering (Demo1, Practical)
Lecture 28 Agglomerative Clustering (Demo2, Practical)
Lecture 29 DBSCAN: Density based method
Lecture 30 DBSCAN- How to select eps (with Practical)
Lecture 31 DBSCAN- Algorithm (with Practical)
Lecture 32 Mean Shift Algorithm
Lecture 33 Mean Shift Algorithm with Practical (1)
Lecture 34 Mean Shift Algorithm with Practical (2)
Lecture 35 K Means Algorithm
Lecture 36 K Means Algorithm with Practical (1)
Lecture 37 K Means Algorithm with Practical (2)
Lecture 38 Association Rules
Lecture 39 Association Rules (with Practical)
Section 4: Supervised Machine Learning
Lecture 40 Supervised Machine Learning - Overview
Lecture 41 Generating Training and Test Data (Train Test Split)
Lecture 42 K Nearest Neighbors (kNN) Algorithm
Lecture 43 kNN Algorithm with Practical
Lecture 44 kNN : Nearest Neighbors Implementation with Practical
Lecture 45 Support Vector Machine (SVM)
Lecture 46 SVM - Practical (linear)
Lecture 47 SVM - Support Vector Regression (SVR) with Practical
Lecture 48 SVM - Mathematics (Hyperplane)
Lecture 49 Non Linear SVM parameters (with Practical)
Lecture 50 SVM kernel trick for not linearly separable data (with Practical)
Lecture 51 Linear Regression
Lecture 52 Linear Regression (with Practical)
Lecture 53 Gradient Descent Overview
Lecture 54 One Hot Encoding (Dummy Variables)
Lecture 55 One Hot Encoding (with Practical)
Lecture 56 Naive Bayes' - Overview
Lecture 57 Naive Bayes' Concept - Demo
Lecture 58 Naive Bayes' - Demo (1)
Lecture 59 Naive Bayes' - Demo (2)
Lecture 60 Naive Bayes' - Assignment
Lecture 61 Logistic Regression - Overview
Lecture 62 Binary Classification, Logistic Regression - Demo
Lecture 63 Multiclass Classification, Logistic Regression - Demo
Lecture 64 ID3 Algorithm - Overview
Lecture 65 ID3 Algo Classifier - Demo
Lecture 66 ID3 Algo Regressor - Demo
Lecture 67 Decision Tree - Overview
Lecture 68 Decision Tree - Demo
Lecture 69 Information about DataSets
python programmers, C/C++ programmers, working of scripting (like jаvascript), fresh developers and intermediate level programmers who want to learn Machine Learning


Homepage

https://www.udemy.com/course/smtbm-ml-py/

   
   
   

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