Getting Started
Course Materials & Solutions
Python Basics
Data Structures in Python
Lists27:51
Tuples24:58
Sets12:46
Dictionaries17:40
Data Structures Quiz
Logic Building in Python
Loops & Iterations19:17
Functions23:31
Map, Reduce & Filter30:15
Control Structures09:39
Logic Building Quiz
File Management & OOPS
File Handling19:20
Object-Oriented Programming (OOPs)20:42
File Management & OOPS Quiz
Scientific Computing
NumPy28:33
Pandas34:13
Scientific Computing Quiz
Data Visualization
Data Visualization Basics06:12
Matplotlib26:37
Seaborn19:11
Data Visualization
Statistics & Data Foundations
Introduction to Statistics05:50
Types of Data (Agenda)00:38
Descriptive Stats08:10
Inferential Stats02:40
Qualitative Data08:33
Quantitative04:17
Sampling & Descriptive Analytics
Sampling Techniques (Agenda)01:37
Population vs Sample03:47
Why Sampling is important03:20
Types of Sampling03:32
Cluster Random Sampling05:12
Probability Sampling08:08
Nonprobability sampling05:18
Population Sampling08:11
Why n-1 and not n05:06
Variability, Bias & Statistical Logic
Descriptive Analytics (Agenda)01:06
Measures of Central Tendency02:30
Mean05:31
Median07:07
Mode04:39
Measures of Dispersion04:21
Range01:43
IQR04:49
Variance Standard Deviation09:56
Mean Deviation04:12
Probability & Bayesian Thinking
Probability (Agenda)00:59
Probability07:09
Addition Rule07:17
Independent Events04:49
Cumulative Probability04:26
Conditional Probability07:57
Bayes Theorem 102:08
Bayes Theorem 204:59
Probability Distributions & Normal Curve
Probability Distrubution (Agenda)02:04
Uniform Distribution08:02
Binomial Distribution12:48
Poisson Distribution03:29
Normal Distribution Part 110:34
Normal Distribution Part 207:38
Skewness04:50
Kurtosis02:36
Calculating Probability with Z-score for Normal Distribution Part 108:12
Calculating Probability with Z-score for Normal Distribution Part 204:45
Calculating Probability with Z-score for Normal Distribution Part 303:33
Covariance & Correlation Analysis
Covariance & Correlation (Agenda)00:31
Covariance12:06
Correlation16:06
Covariance VS Correlation03:57
Hypothesis Testing & Statistical Inference
Hypothesis Testing06:59
P Value04:14
T Test07:32
Tailed Tests02:37
Types of Test08:22
Z Test09:24
Chi Square Test08:58
ANOVA08:56
Correlation Test (Practicals)06:57
Data Analytics & EDA Overview
Agenda02:32
DA, DS Processes06:35
What is EDA03:08
Visualization04:49
EDA Process & Data Preparation
Steps involved in EDA (Data Sourcing)04:35
Steps involved in EDA (Data Cleaning)04:11
Handle Missing Values (Theory)06:25
Handle Missing Values (Practicals)11:35
Feature Scaling & Outlier Treatment
Feature Scaling (Theory)11:04
Standardization Example04:13
Normalization Example02:30
Feature Scaling (Practicals)13:50
Outlier Treatment (Theory)09:21
Outlier Treatment (Practicals)13:41
Invalid Data04:47
Data Types & Analysis Techniques
Types of Data02:57
Types of Analysis02:35
Univariate Analysis09:00
Bivariate Analysis05:15
Multivariate Analysis01:21
Numerical Analysis03:56
Analysis Practicals30:14
Feature Engineering
Derived Metrics04:33
Feature Binning (Theory)07:17
Feature Binning (Practicals)10:48
Feature Encoding (Theory)12:25
Feature Encoding (Practicals)21:32
EDA Case Study & Reporting
Case Study18:15
Data Exploration19:16
Data Cleaning09:45
Univariate Analysis14:17
Bivariate Analysis Part 114:59
Bivariate Analysis Part 208:50
EDA Report05:14
Introduction to Machine Learning
Agenda02:25
Introduction to ML04:51
Types of ML18:17
Use Cases Part 102:10
Use Cases Part 201:28
Pre-Requisites
Features11:37
Train-Test Split18:04
Feature Scaling11:04
Standardization Example04:13
Normalization Example02:30
Feature Encoding12:25
Feature Encoding (Practicals)10:46
Regression
Introduction to Regression Models05:44
Regression Metrics35:20
Regression Metrics (Practicals)18:18
Simple Linear Regression15:44
Multiple Linear Regression11:33
Linear Regression (Practicals)26:23
Multiple Linear Regression (Practicals)13:24
Polynomial Regression10:10
Polynomial Regression (Practicals)26:30
Bias Variance Tradeoff09:15
Ridge Regression13:31
Lasso Regression09:06
Lasso & Ridge Regression (Practicals)44:53
Classification
Introduction to Classification06:04
Types of Classification06:39
Log Loss15:53
Confusion Matrix13:00
AUC ROC Curve09:14
Classification Report07:39
Classification Report07:39
KNN Classifier11:01
KNN Classifier Example10:20
Practicals Part 114:31
KNN Classifier (Practicals)14:16
Decision Tree08:38
Decision Tree (Entropy based)14:17
Decision Tree (gini based)20:08
Decision Tree (Practicals)08:02
Decision Tree (Visualizing)21:04
Random Forest Classifier05:56
Random Forest Classifier (Practicals)05:05
Naive Bayes Classifier14:45
SVM Classifier Part 114:49
SVM Classifier Part 214:29
Logistic Regression19:02
Practicals so far27:48
Issues in Classification (Part 1)08:21
Issues in Classification (Part 2)11:00
Project35:15
Ensemble Learning
Introduction to Ensemble Learning28:31
Bagging15:55
Bagging vs Random Forest13:23
Bagging (Practicals #1)31:09
Bagging (Practicals #2)20:31
Boosting11:01
Ada Boost23:18
Gradient Boost06:40
CF vs LF10:12
Cross Entropy06:47
Xtreme Gradient Boosting (XGB)18:36
Project24:51
Clustering
Introduction to Clustering16:37
K-Means Clustering20:29
K-Means Clustering (Practicals)19:53
Hierarchical Clustering15:48
Hierarchical Clustering (Practicals)14:02
Mean Shift Clustering11:28
Feature Engineering
Introduction13:15
RFE and SFS06:39
RFE (Practicals)23:18
Successive Feature Selection19:30
Chi-Square06:03
Chi-Square (Practicals)06:19
Principal Component Analysis34:47
Principal Component Analysis (Practicals)10:26
Linear Discriminant Analysis09:52
Linear Discriminant Analysis (Practicals)09:31
KPCA & QDA06:14
KPCA & QDA (Practicals)05:41
Hyper Parameter Optimization
Basics10:05
Manual HPO06:07
GridSearch vs RandomizedSearch10:58
Manual HPO (Practicals)22:12
RandomizedSearchCV (Practicals)18:18
GridSearchCV (Practicals)08:20
Deployment
Deployment Basics07:27
Introduction to Flask12:41
Flask Basic App12:37
Model Building (Breast Cancer Prediction)15:55
Flask App (Breast Cancer Prediction)22:23
AWS09:52
AWS Deployment (Breast Cancer Prediction)28:51
Conclusion
Thank You
Variables & Keywords
