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Basics of Machine Learning
Machine Learning Basics for a Newbie
Machine Learning Lifecycle
6 Steps of Machine learning Lifecycle
Introduction to Predictive Modeling
Importance of Stats and EDA
Introduction to Exploratory Data Analysis & Data Insights
Descriptive Statistics
Inferential Statistics
How to Understand Population Distributions?
Understanding Data
Reading Data Files into Python
Different Variable Datatypes
Probability
Probability for Data Science
Basic Concepts of Probability
Axioms of Probability
Conditional Probability
Exploring Continuous Variable
Central Tendencies for Continuous Variables
Spread of Data
KDE plots for Continuous Variable
Overview of Distribution for Continuous variables
Normal Distribution
Skewed Distribution
Skeweness and Kurtosis
Distribution for Continuous Variable
Exploring Categorical Variables
Central Tendencies for Categorical Variables
Understanding Discrete Distributions
Performing EDA on Categorical Variables
Missing Values and Outliers
Dealing with Missing Values
Understanding Outliers
Identifying Outliers in Data
Outlier Detection in Python
Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Central Limit theorem
Sample and Population
Central Limit Theorem
Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Bivariate Analysis Introduction
Continuous - Continuous Variables
Covariance
Pearson Correlation
Spearman's Correlation & Kendall's Tau
Correlation versus Causation
Tabular and Graphical methods for Bivariate Analysis
Performing Bivariate Analysis on Continuous-Continuous Variables
Continuous Categorical
Tabular and Graphical methods for Continuous-Categorical Variables
Introduction to Hypothesis Testing
P-value
Two sample Z-test
T-test
T-test vs Z-test
Performing Bivariate Analysis on Continuous-Catagorical variables
Categorical Categorical
Chi-Squares Test
Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis
Multivariate Analysis
A Comprehensive Guide to Data Exploration
The Data Science behind IPL
Different tasks in Machine Learning
Supervised Learning vs Unsupervised Learning
Reinforcement Learning
Generative and Descriminative Models
Parametric and Non Parametric model
Build Your First Predictive Model
Machine Learning Pipeline
Preparing Dataset
Build a Benchmark Model: Regression
Build a Benchmark Model: Classification
Evaluation Metrics
Evaluation Metrics for Machine Learning Everyone should know
Confusion Matrix
Accuracy
Precision and Recall
AUC-ROC
Log Loss
R2 and Adjusted R2
Preprocessing Data
Dealing with Missing Values