Data Science & Machine Learning Course Details:

• Introduction to machine learning

• Relationship between Artificial Intelligence, Machine Learning, and Data Science

• Representations of Patterns and Classes

• Designing a learning system and various components of it

• Application areas and present market demand from a multi-disciplinary point of view

• Data Preprocessing, Importing the dataset, Missing Data, Categorical Data, Feature scaling

• Accumulation of feature-set from a practical problem domain and their subsequent graphical representation in N-dimensional feature space

• Curse of dimensionality, feature extraction and selection strategies

• Filtering and wrapping models of feature selection

• Sequential forward and backward selection methods

• Feature ranking

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• SVR

• Decision Tree Regression

• Bagging strategy – bootstrapping, random sampling with repetition and aggregation of outcomes

• Random forest Regression

• Feature ranking using random forest

• Performance evaluation – confusion and cost matrix and its associated measurement metrics like specificity, sensitivity, accuracy etc.

• Logistic Regression

• K-Nearest Neighbors (K-NN)

• Support Vector Machine (SVM), Kernel SVM

• Naive Bayes

• Decision Tree Classification

• Random Forest Classification

• Evaluating Classification Models Performance

• K-Means Clustering

• Hierarchical Clustering

• Apriori

• Eclat

• Upper Confidence Bound (UCB)

• Thompson Sampling

• ​ Detail Discussion on NLP

• Artificial Neural Networks: Computational model of a neuron, synaptic weights and various activation functions, Network architectures with forward and backward flow of information.

• Linear perceptron model

• Evolution of Multilayer perceptron model and Backpropagation algorithm with mathematical derivation to optimize the error

• Convolutional Neural Networks

• Principal Component Analysis (PCA)

• Linear Discriminant Analysis (LDA)

• Introduction to Model Selection & Boosting

• Model Selection

• k-Fold Cross Validation

• Grid Search

• XGBoost

Real time project on industrial dataset