Best React.Js Training & Courses In Kochi, Kerala|Expertzlab

Machine Learning (2 Months)

Statistics for data science, Response variables, Mean, Variance, Covariance, Beta, Alpha, Distributions

Normal Distribution, Link function, Logistic function, Logit function, Binomial, Poisoon, Chi Test, Anova, Use cases

SKlearn, Regression, Linear Regression, Multi-linear Regression, Classification

Navebays classification, Decision Tree, Random Forest, KNN, Clustering, K-Means,

Training data sets, over-fitting, under-fitting, cross-validation, Regularities

Variance, Bias, Performance measures, Bias Variance Tradeoff

Accuracy, Precesion, Recall, Sensitivity, Estimator, Visualization of training results

linear least squares, cost or loss function, Residuals, Residual sum of squares

least squares, Evalulating Model, calculate R-Squared, Regression Vector, Evolving Model

Finding Beta Vector, Calculating coefficients, Finding performance, Polynomial Regression

Compare Polynomial and Linear Regression, Quadratic and Cubic difference, regression curve

Nth Degree Polinomial, Regularization, Ridge Regression, Effect of Ridge regression

Apply Ridge regression, Lasso Regression, Elastic Net Regression, train/test split

Fitting evaluation technique, Validation, k-fold cross validation, Leave one out cross validation

Gradient Descent, Derivative of loss function, Learning rate, when to use GD

Stochastic Gradient Descent, Scaling features, Feature Extraction and Preprocessing

Label encoder, One-Hot encode, Extracting features from text, bag-of-words, Corpus, NLTK, Text processing, WordVect

Feature Vector, Tokenization, Document Similarity, Euclidian Distance, Semantic equivalent of documents

Sparse Vectors, Curse of Dimensionality, Hughes effect, Effect of adding dimensionality

Stop word filterning, Stemming and lemmatisation, Determiningl Lemma, TF-IDF weights, TF-IDF featurization

Logarithmically scalled frequencies, Augmented frequencies, Normalization, Inverse Document Frequency

hash trick, HashVectorizer, Computer Vision, Face detection, Extracting features from pixel densities

OCR, Hanwritten letter recognition, Feature vector of an image, Image featurization, Deep Learning from images

Extracting points of objects, Point of interestes, SIFT, SURF, mohotas, Data Standardization, Normalization

Unit scaling, z-score scaling, Standard scaler, Domination Feature, Logistic Regression

Binary classification, Confusion Matrix, Visualiz confusion matrix, Reading confusion matrix, Accuracy

Precesion and Recall, F1 measure, ROC AUC, Plot AUC, Hperparameters, grid search, tuning model

Multi-class classification, Sentimental Analysis, Medical data analysis, Multi-label classification performance

Jaccard Similarity, Hamming Loss, Non Linear classification and Regression, agglomerative and divisive clustering

Building Trees, Similarity Impact, Purity of Decesion Trees, Binary classification problem use case

Measure of Purity, Gini Impurity, Classification error, Training decesion trees, Entropy, Gain Ratio and over fitting

Pruning, Post Pruning, Titanic Disastor Analytics Use case, Data Exploration, Modeling, Decision Tree classifier

Render DT Pic, Ensemble, Random guessing, Ada Boost, Tree ensembling, Random input and combinations

Bagging, Boosting, Dimensionality Reduction, Extremely Randomised Trees, Overfitting RF, Stacking and Blending

Stacked encoder, Eager Learners, Lazy Learners, Classification Distortions, Local optima, elbow method, Evaluating clusters

silhouette coefficient, Image quantization, Dimensionality Reduction PCA, Effect of dimensionality reduction

Eigenvalues, Linear transformations, DR preprocess, project of DR, DR illustration, coordinate transformation

Managing information loss, coponent split, ScreePlot, face recognition, Singular value Decomposition, SVD works

Mathematical form of SVD, SVD DR, Reconstruction of noisy image, Reconstruction graph, Recommendation systems

Content based filtering, Cosine similarity, recommendation function, collaborative filtering itembased and model based

Estimating missing values, Estimate error, SVM, classification boundary, Goal of SVM, Maximum margin

Support Vectors, mathematical analysis, margin, optimisation function, Soft margin, constraints and margin

conditions for finding margin, Deduce from equations, Hyper plane, Kernel Trick, Mapping to Higher Dimensional plane

Kernel calculation, applying higher dimensional space, Kernel functions, Sequential Minimal optimisation, classifying characters use case

Recognise handwritten letters, SVM Regression, SVM classes

Machine Learning Syllabus

Machine Learning (2 Months)

Detail Syllabus:

Quick Links:

Institute Hours:

Contact Details:

Address:

Phone:

Email: