Development Online Course by Udemy, On Sale Here
Learn to implement classification and clustering algorithms using MATLAB with practical examples, projects and datasets
An excellent training about Data Science
Machine Learning for Data Science using MATLAB
Basic Course DescriptionThis courseis for youif you want tohave a real feel of theMachine Learning techniqueswithout having tolearnall the complicatedmaths. Additionally, thiscourse is also for youif you have had previous hours and hours ofmachine learning theorybut could never got a change orfigure outhow to implement andsolve data science problems with it. The approach in this course is very practicalandwe will start everything from veryscratch. We will immediately start coding after a couple of introductory tutorialsand we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages forengineer and science studentsand is frequently used by top data science research groups world wide. Below is the briefoutline of this course. Segment 1: Introduction to course In this section we spend some time talking about the topics youll learn, the approach of learning used in the course, essential details about MATLAB to get you started. This will give you an idea of what to expect from the course. Segment 2: Data preprocessing(Brief videos)We need to prepare and preprocess our data before applying Data Science algorithms and techniques. This section discusses the essential preprocessing techniques and discuses the topics such as getting rid of outliers, dealing with missing values, converting categorical data to numerical form, and feature scalling. Segment 3: Classification Algorithms in MATLABClassification algorithms is an important class of Data Science algorithms and is a must learn for every data scientist. This section provides not only the intuition behind some of the most commonly used classification algorithm but also provides there implementation in MATLAB. The algorithms that we cover areK-Nearest NeighborNave BayesainSupport Vector MachineDecision TreesDiscriminant AnalysisEnsemblesIn addition to these we also cover how to evaluate the performance of classifiers using different metrics. Segment 4: Clustering Algorithms in MATLABThis section introduces some of the commonly used clustering algorithms alongside with their intuition and implementation in MATLAB. We also cover the limitations of clustering algorithms by looking at their performance when the clusters are of different sizes, shapes and densities. The algorithms we cover in this section are K-MeansMean ShiftDBSCAN Hierarchical Clustering In the same section, we also cover practical application of the clustering algorithms by looking at the applications of image compression and sentence grouping. This section provides some intuition regarding the strengths of clustering in real life data analysis tasks. Segment 5: Dimensionality ReductionDimensionality reduction is an important branch of algorithms in Data Science. In this section we show how to reduce the dimensions for a specific Data Science problems so that the visualization becomes easy. We cover the PCA algorithm in this section. Segment 6: Project: Malware AnalysisIn this section we provide a detailed project on malware analysis from one of our recent research paper. We provide introductory videos on how to complete the project. This will provide you with some hands on experience for analyzing Data Science problems. Segment 7: Data preprocessing(Detailed Videos)In this section we dive deep into the topic of data preprocessing and cover many interesting topics. The topic in this section include Dealing with missing data using Deleting strategiesUsing mean and modeRadom values for handling missing dataClass based strategiesConsidering as a special valueDealing with Categorical Variables using the One hot encodingFrequency based encodingTarget based encodingEncoding in the presence of an orderOutlier Detection using3 sigma rule withBox plot ruleHistogram based ruleLocal outlier factorOutliers in categorical variableFeature Scaling and Data Discretization Your Benefits and Advantages: If you do not find the course useful, you are covered with30 day money back guarantee, full refund, no questions asked! You will be sure of receiving quality contents since the instructorshas already manycoursesin the MATLAB on udemy. You havelifetime access to the course. You haveinstant and free access to any updatesi add to the course. You have access to allQuestions anddiscussionsinitiated by other students. You will receivemy supportregarding any issues related to the course. Check out the curriculum andFreely available lecturesfor a quick insight. It’s time to takeAction! Click the “Take This Course” button at the top right now. Time is limited andEvery second of every day is valuable. We are excited to see youin the course! Best Regrads, Dr. Nouman Azam More Benefits and Advantages: You receiveknowledge from an experiencedinstructor(Dr. Nouman Azam) who is thecreatoroffivecourses on Udemy in the MATLAB niche. The titles of these courses areComplete MATLAB Tutorial: Go from Beginner to ProMATLAB App Desigining: The Ultimate Guide for MATLAB AppsGo From Zero to Expert in Building Regular ExpressionsMaster Clu
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