AI & DS

Linear Regression (Least Squares Error, Gradient Descent, Regularization)

Support Vector Machine (Classifier, Maximal Margin Hyperplane, Support Vectors, Kernel, Sample Code)

Principal component analysis (Steps, Eigenvalues, Eigenvectors, Calculations, Dimension Reduction)

Artificial Neural Network 4 (Activation Functions, Sigmoid, tanh, ReLu, Leaky ReLu)

Artificial Neural Network 3 (Backward propagation, cost function, gradient descent)

Artificial Neural Network 2 (Stepwise Forward Propagation)

Artificial Neural Network 1 (Introduction, types, benefits and applications, architecture, steps)

Time Series & Forecast (components, Models, Methods, Averaging, Exponential Smoothing, R-Code)

Market Basket Analysis (Association Rules, Support, Confidence, Lift, Conviction, Sequence Mining)

Text Analytics (Text mining, Feature extraction, Pre-processing, tf-idf, R-codes)

Clustering (Hierarchical Clustering and Non-Hierarchical Clustering, K-means, Similarity functions)

Logistic Regression (Cost, Gradient Descent Function, ROC Curve, Odds, logits, multiclass, binomial)

Probability Distribution (Normal Distribution, Chi-square Distribution)

Data Cleansing and Processing 1 (Missing Data, Imputation, Dummy Variables, Outliers, Skewness)

Understanding Data1 - Data Types, Descriptive Statistics

Understanding Data 2 (Descriptive Analysis, Data Summary, Correlation, Outliers, R Special Values )

R Programming 5 (Loops, functions, conditional statements and graphics)

R Programming 4 (workspace management, data types, saving, retrieving files)

R Programming 3 (Creating and Using Matrices)

R Programming 2 (Creating and Using Variables and Vectors)

R Programming Part 1

Jargons of Data Science - DS Terminology

What is Data Science?