Getting Started
Course Materials & Solutions
Python Basics
Data Structures in Python
Lists27:52
Tuples24:58
Sets12:45
Dictionaries17:40
Data Structures Quiz
Logic Building in Python
Loops & Iterations19:17
Functions23:32
Map, Reduce & Filter30:16
Control Structures09:39
Logic Building Quiz
File Management & OOPS
File Handling19:20
Object-Oriented Programming (OOPs)20:43
File Management & OOPS Quiz
Scientific Computing
NumPy28:34
Pandas34:13
Scientific Computing Quiz
Data Visualization
Data Visualization Basics06:13
Matplotlib26:37
Seaborn19:11
Data Visualization
Statistics & Data Foundations
Introduction to Statistics05:50
Types of Data (Agenda)00:39
Descriptive Stats08:11
Inferential Stats02:42
Qualitative Data08:34
Quantitative04:17
Sampling & Descriptive Analytics
Sampling Techniques (Agenda)01:37
Population vs Sample03:47
Why Sampling is important03:20
Types of Sampling03:33
Cluster Random Sampling05:13
Probability Sampling08:08
Nonprobability sampling05:18
Population Sampling08:11
Why n-1 and not n05:06
Variability, Bias & Statistical Logic
Descriptive Analytics (Agenda)01:07
Measures of Central Tendency02:30
Mean05:32
Median07:07
Mode04:39
Measures of Dispersion04:22
Range01:44
IQR04:50
Variance Standard Deviation09:56
Mean Deviation04:13
Probability & Bayesian Thinking
Probability (Agenda)00:59
Probability07:10
Addition Rule07:18
Independent Events04:40
Cumulative Probability04:26
Conditional Probability07:57
Bayes Theorem 102:08
Bayes Theorem 205:00
Probability Distributions & Normal Curve
Probability Distrubution (Agenda)02:05
Uniform Distribution08:02
Binomial Distribution12:48
Poisson Distribution03:29
Normal Distribution Part 110:35
Normal Distribution Part 207:39
Skewness04:51
Kurtosis02:36
Calculating Probability with Z-score for Normal Distribution Part 108:12
Calculating Probability with Z-score for Normal Distribution Part 204:46
Calculating Probability with Z-score for Normal Distribution Part 303:33
Covariance & Correlation Analysis
Covariance & Correlation (Agenda)00:31
Covariance12:07
Correlation16:07
Covariance VS Correlation03:58
Hypothesis Testing & Statistical Inference
Hypothesis Testing06:59
P Value04:14
T Test07:33
Tailed Tests02:37
Types of Test08:23
Z Test09:25
Chi Square Test08:59
ANOVA08:57
Correlation Test (Practicals)06:58
Data Analytics & EDA Overview
Agenda02:32
DA, DS Processes06:36
What is EDA03:08
Visualization04:49
EDA Process & Data Preparation
Steps involved in EDA (Data Sourcing)04:36
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 Example04:14
Normalization Example02:30
Feature Scaling (Practicals)13:50
Outlier Treatment (Theory)09:21
Outlier Treatment (Practicals)13:42
Invalid Data04:48
Data Types & Analysis Techniques
Types of Data02:57
Types of Analysis02:36
Univariate Analysis09:01
Bivariate Analysis05:16
Multivariate Analysis01:22
Numerical Analysis03:56
Analysis Practicals30:15
Feature Engineering
Derived Metrics04:33
Feature Binning (Theory)07:18
Feature Binning (Practicals)10:48
Feature Encoding (Theory)12:26
Feature Encoding (Practicals)10:47
EDA Case Study & Reporting
Case Study18:15
Data Exploration19:17
Data Cleaning09:46
Univariate Analysis14:18
Bivariate Analysis Part 115:00
Bivariate Analysis Part 208:50
EDA Report05:14
Introduction to Machine Learning
Agenda02:24
Introduction to ML04:52
Types of ML18:17
Use Cases Part 102:11
Use Cases Part 201:28
Pre-Requisites
Features11:38
Train-Test Split18:04
Feature Scaling11:04
Standardization Example04:14
Normalization Example02:30
Feature Encoding12:26
Feature Encoding (Practicals)10:47
Regression
Introduction to Regression Models05:45
Regression Metrics35:20
Regression Metrics (Practicals)18:19
Simple Linear Regression15:44
Multiple Linear Regression11:34
Linear Regression (Practicals)26:14
Multiple Linear Regression (Practicals)13:25
Polynomial Regression10:10
Polynomial Regression (Practicals)26:30
Bias Variance Tradeoff09:15
Ridge Regression13:32
Lasso Regression09:07
Lasso & Ridge Regression (Practicals)44:53
Classification
Introduction to Classification06:04
Types of Classification06:39
Log Loss15:54
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)14:18
Decision Tree (gini based)20:09
Decision Tree (Practicals)08:03
Decision Tree (Visualizing)21:04
Random Forest Classifier05:56
Random Forest Classifier (Practicals)05:06
Naive Bayes Classifier14:45
SVM Classifier Part 115:00
SVM Classifier Part 214:30
Logistic Regression19:02
Practicals so far27:48
Issues in Classification (Part 1)08:21
Issues in Classification (Part 2)11:01
Project35:14
Ensemble Learning
Introduction to Ensemble Learning28:31
Bagging15:55
Bagging vs Random Forest13:23
Bagging (Practicals #1)31:10
Bagging (Practicals #2)20:32
Boosting11:01
Ada Boost23:18
Gradient Boost06:40
CF vs LF10:12
Cross Entropy06:48
Xtreme Gradient Boosting (XGB)18:37
Project24:52
Clustering
Introduction to Clustering16:37
K-Means Clustering20:30
K-Means Clustering (Practicals)19:54
Hierarchical Clustering15:49
Hierarchical Clustering (Practicals)14:03
Mean Shift Clustering11:28
Feature Engineering
Introduction13:16
RFE and SFS06:40
RFE (Practicals)23:19
Successive Feature Selection19:30
Chi-Square06:04
Chi-Square (Practicals)06:20
Principal Component Analysis34:47
Principal Component Analysis (Practicals)10:27
Linear Discriminant Analysis09:53
Linear Discriminant Analysis (Practicals)09:31
KPCA & QDA06:15
KPCA & QDA (Practicals)05:42
Hyper Parameter Optimization
Basics10:06
Manual HPO06:08
GridSearch vs RandomizedSearch10:58
Manual HPO (Practicals)22:13
RandomizedSearchCV (Practicals)18:19
GridSearchCV (Practicals)08:21
Deployment
Deployment Basics07:28
Introduction to Flask12:41
Flask Basic App12:37
Model Building (Breast Cancer Prediction)15:56
Flask App (Breast Cancer Prediction)22:24
AWS09:52
AWS Deployment (Breast Cancer Prediction)28:52
Conclusion
Thank You
Introduction to Python
