You will work on various Python libraries such asPandas, Scikit, NumPy, Matplotlib, etc.
Introduction to Data Science – Introduction to Machine Learning – What is Python – Role ofPython in Machine Learning and Python –Installing Python – Python IDE’s – JupyterNotebook Installation and usage.
What is package – Advantage of usingpackages over core Python – Pandas – Numpy– Sci-kit Learn – Mat-plot library.
Selecting rows / observations – RoundingNumbers – Selecting columns and fields –Merging data – Data aggregation – Datamunging techniques.
Probability Basics – What is meant byprobability, Types, ODDS ratio – StandardDeviation – Data deviation andDistribution , Variance. Bias variantTradeoff – Underfitting, Overfitting.Distance metrics - Euclidean Distance,Manhattan Distance. Outlier analysis -What is an Outlier?, Inter Quartile Range,Box & whisker plot, Upper Whisker, LowerWhisker, Scatter plot, Cook’s Distance, -Missing Value treatment - What is NA?,Central Imputation, KNN imputation,Dummification. Correlation - Pearsoncorrelation, positive & Negativecorrelation.
Supervised Learning - Linear Regression,Linear Equation, Slope, Intercept, R squarevalue, Logistic regression, ODDS ratio,Probability of success, Probability of failureBias Variance Tradeoff, ROC curve, BiasVariance Tradeoff –
Unsupervised Learning - K-Means, K-Means ++, Hierarchical Clustering - SVM -Support Vectors, Hyperplanes, 2-D Case,Linear Hyperplane - SVM Kernal – Linear,Radial, Polynomial
Select query - Sub Query - Regular Expressions Functions andConditions in SQL - Date and Time in SQL - SQL Privileges
