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Machine Learning

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Published in: Machine Learning
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This document consists of machine learning introduction and mindmap required to achieve various paths in machine learning

Sravani Y / Hyderabad

5 years of teaching experience

Qualification: Masters in CSE

Teaches: Basic Computer, Computer for official job, MS Office, School Level Computer, All Subjects, Computer, English, EVS, Geography, Hindi, History, Language, Mathematics, Science, Algebra, Business Mathematics, Chemistry, Computer Science, IT & Computer Subjects, Logic, Physics, Statistics, Biology, Economics, Social Studies, Political Science, Bharat Natyam, Kuchipudi, Electronics And Communication, IT, Software Engineering, C / C++, Java And J2EE, Python Programming, Shell Scripting, Java API, Advanced Maths, Coding & Programming, Scratch Programming, Matlab, Spring Training, Unix/Linux, Java Script, Node JS, React JS

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  1. MACHINE @ml.india LEARNING INDIA MACHINE LEARNING ALGORITHMS Hit to support! 1 of 10
  2. @ml.india ZXhaus-Hue lisH Machine Learning India This post is an attempt to provide an exhaustive lis of machine learning algorithms and methods. We understand that getting started with machine learning can be enervating and finding the right algorithm or technique could be deceptive, and therefore we hope that this summary gives you all - a baseline to choose the right algorithm for your requirements. Note: Most of the listed algorithms are versatile and multipurpose. Hit to support! 2 of 10 >
  3. @ml.india Machine Learning India •Regression and gequlaTi3a+ion e Ordinary least squares regression. Linear regression. Polynomial regression. Stepwise regression. Locally estimated scatter-plot smoothing. Su pport vector regression. Multivariate adaptive regression splines. Quantile regression. Ridge regression. LASSO regression. Elastic-net regression. Least-angled regression. Hit to support! 3 of 10 >
  4. @ml.india Bayesian Algoyi+hmS: Naive Bayes. Machine Learning India Averages one-dependence estimators. Bayesian belief networks. Gaussian naive Bayes. Multinomial naive Bayes. Bayesian networks. Trees and ensembles Classification and regression trees. Iterative dichotomisers. Decision stump. Random forests. Gradient boosting machines. Boosting. Bootstrapped aggregation (bagging). Ada-boost. Stacked generalization (blending). Hit to support! 4 of 10 >
  5. @ml.india Machine Learning India Dimensionaliåy •Reduc+ion•. e Principal component analysis. e Sammon mapping. Multidimensional scaling. Projection pursuit. o Linear discriminant analysis. e Partial least squares regression. C.Au*eTinq and in*ance-based. e K-means clustering. K-medians clustering. Expectation maximization. Hierarchical clustering. K-nearest neighbor. Self-organ izing maps. Learning vector quantization. Locally-weighted learning. Hit to support! 5 of 10 >
  6. @ml.india Machine Learning India OHneT significan+ algoTihmS: Su pport vector machines. Logistic regression. Perceptron. Gradient descent. Backpropagation. Apriori algorithm. FP-Growth. Hidden Markov models. Poisson regression. Alternating least-squares. Gaussian mixture models. DBSCAN. Singular value decomposition. Latent Dirichlet analysis. Hit to support! 6 of 10 >
  7. @ml.india Machine Learning India comp*a+ional complexi-Hes: Algorithm Classification/Regression Training Decision Tree Random Forest Random Forest Random Forest Extremly Random Trees C+R Gradient Boosting (ntrees ) Linear Regression SVM (Kernel) k-Nearest Neighbours (naive) Nearest centroid Neural Network Naive Bayes C+R C+R R Breiman implementation C Breiman implementation C+R C+R C+R c C+R c O (n2p) O (n2pntrees) O (n2pntrees) Prediction O(p) O(pntrees) O(pntrees) O(n2vfintrees) O(pntrees) O (npntrees) O (npntrees) O(p2n + *03) O(n2p + n3) O (np) O(np) O(npntrees) O(pntrees) O(p) O(nsvP) O(np) O(p) O(pnll 4- ) Source: The Future of Computation for Machine Learning and Data Science on https://www.experfy.com/ Hit to support! 7 of 10 >
  8. @ml.india Machine Learning India Easier TemembeT: DBSCAN K-Mea Agglomer MearÆhift Clustering uZZY C•MeanS Eucl at Apriori Pattern search Naive Bayes Decision Trees Classification Logistic Regression L Regression Regression P 01 ynomial Fp-GroW th DIMENSION RENCTION (generalization LDA PCA LSA svD REINFORCEMENT LEARNING utasuPERVlSED suPERVISED Genetic Algo rithm Q -L ea rning CLASSICAL LEARNING MACHINE LEARNING NEURAL NETS AND DEEP LEARNING Regression Ridge/Lasso Regression F oreSt agging ENSEMBLE METHODS XGB00St Boost SARSA Deep Work Neural tetworks Z (CNN) Dcuu AdaBoost LightGBM Ca tB00St perceptrons (MCP) Recurrent Neural orks (Rt•N) Generative Adversarial uetworks Image by vas3k. Hit to support! 8 of 10 >
  9. @ml.india Some references: Machine Learning India - Machine Learning algorithms mind-map by MachineLearningMastery.com. - Which Machine LearningAlgorithm Should I Use? by Hui Li on KDNuggets.com. O no-Ye: The links to these resources will be put up on our Telegram. Channel ID: @machinelearning24x7. Hit to support! 9 of 10 >
  10. @ml.india Machine Learning India MACHINE LEARNING INDIA Whaå- did We miss? Let us know in the comments! If you like our content and find it valuable, smash that follow button! Your love and support inspires us to keep delivering the best we can! o 10 of 10 e

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