# DATA SCIENCE AND ML INTERMEDIATE WORKSHOP : COURSE OUTLINE

Duration: 3 Days

Introductions, Stats, Prob, Python, Data Manipulation, Visualization (Day 1)

● Introduction to Data Science and Understanding of problem Statement

● Basic Statistics – Measures of Central Tendencies and Variance

● Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

● Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests (One sample, independent, paired), Analysis of variance, Correlations and Chi-square

● Important modules for statistical methods: NumPy, SciPy, Pandas

● Using Statistical methods on visualization and understanding concepts

● Treatment of Data

● Data Manipulation

Supervised Learning (Day 2)

● What is Linear & Non –Linear

● Different types of Data – Numerical & Categorical

● Vector Space , Mathematical functions Dimensions and Their Graphical/Vector Representation

● Introduction to Machine Learning & What is Model

● Types of ML problem

● Model and Curve

● Linear Regression & Equation

● LR Solvers – OLS method

● LR Solvers – Gradient Descent

● Assumptions of LR

● Evaluation metrics of LR

● Advance LR concept , Non-linear L1 & L2

● Case-study

● Basics of probability and Odds

● Classification using Linear Regressions

● Logit Equation and Logit function to solve the Classification

● Classification Evaluation – Accuracy, Confusion Matrix, Precision, Recall & F1

● ROC and AUC curve

● Feature Engineering & Feature Selection in ML Algorithms

● Model Interpretability using SHAP

● Use-cases

Unsupervised Learning (Day 3)

● Introduction to Unsupervised Learning

● Concepts behind Unsupervised techniques and understanding according to business use-cases

● Clustering & Segmentations in ML

● K-means Clustering technique

● Spectral Clustering , DBSCAN & Optics algorithms for clustering

● Multi-Cluster Algorithm Analysis of Unsupervised Problems

● Evaluation Metrics for Clustering

● Use-Cases

© Benzne. All Rights Reserved.