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Registered with Ministry Of Corporate Affairs - Government of India,
(CIN: U72900KL2016PTC046479, Dated: 05/08/2016)
Machine Learning Syllabus
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