Overview
Course Outline
Introduction
Python for Data Science
Introduction to Python
Python installation & configuration
Python Features
Basic Python Syntax with implementation
Statements, Indentation, and Comments
Data Analytics using R
Introduction to R
RStudio installation & configuration
Basic Python Syntax
Basic visualization and data analysis
Statistics and Mathematics for Machine Learning
Statistical Inference
Descriptive Statistics
Introduction to Probability, Conditional probability, Bayes theorem
Probability Distribution
Introduction to inferential statistics
Normality, Normal Distribution
Measures of Central Tendencies
Hypothesis Testing
Data visualization using python
Machine Learning in Python
Machine Learning introduction
Machine Learning applications & use-cases
Machine Learning Flow
Machine Learning categories
Exploratory data analysis
Data cleaning and Imputation Techniques
Linear regression
Gradient descent
Model evaluation
Supervised Learning
What is Supervised Learning?
Logistic Regression in Python
Classification & implementations
Decision Tree
Different algorithms for Decision Tree Induction
How to create a Perfect Decision Tree
Confusion Matrix
Random Forest
Tree based Ensemble
Hyper-parameter tuning
Evaluating model output
Naive Bayes Classifier
Support Vector Machine
Unsupervised Learning
What is Unsupervised Learning
Clustering
K-means Clustering
Hierarchical Clustering
Data Mining
Association Rules
Recommendation Engines
Module1: Comprehending and creating the SIPOC Diagram
Module 2: Overview of Process Mapping
Module 3: C&E Matrix
Module 4: Effects Analysis along with Failure Modes
Module 5: Fundamental Statistics
Module 6: Minitab Introduction
Module 7: Developing Graphs (fundamental quality tools)
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