arrow_back
overview
Introduction
0.1-Course Introduction
0.2-Chapter-wise Introduction
Requirements Analysis
1.0-Introduction to Requirements Analysis
1.1-Eliciting
1.2-Threats to validity
1.3-Analysing
1.4-Requirements modeling
1.5-Reviews & Retrospective
Data
2.1-Data Sources
2.2-Data Collection
Preprocessing
3.0-Preprocessing- Introduction
3.1-Preprocessing
3.2-Data Cleansing
3.3-Data Integration
3.4-Data Transformation
3.5-Data Reduction
3.6-Errors
3.7-Error- Definition
3.8-Error sources
3.9-Systematic errors
3.10-Instrumental, Environmental, Procedural errors
3.11-Human error
3.12-Effects of poor data quality
3.13-Data cleansing techniques
3.14-Reducing random error
3.15-Data cleansing- more insight
3.16-Data redundancy
3.17-Feature extraction
Data Understanding
4.0-Data Understanding
4.1-Introduction
4.2-Data attributes
4.3-Key Characteristics and Outliers
4.4-Normal distribution
4.5-Skewness
4.6-Skewness- Mean, Median & Mode
4.7-Kurtosis
4.8-Rank Ordered Statistics
4.9-Histogram
4.10-Box plot
4.11-More on Box plot
4.12-Applications of box plot
4.13-Scatter plot
4.14-Plot types
Model Building - Regression
5.0-Model building
5.1-Predictive model
5.2-Regression Vs Classification
5.3-More on Classification, Regrassion
5.4-Regression VS Classification summary
5.5-Regression
5.6-Example for regression
5.7-Regression example continued
5.8-Regression process
5.9-Regression model
5.10-Correlation
5.11-Regression types
5.12- types of regression
5.13-Linear regression
5.14-Ordinary Least Square (OLS)
5.15-OLS Assumption 1
5.16-OLS Assumptions 2 &3
5.17-OLS Assumptions 4,5 & 6
5.18-OLS Assumptions- Summary
5.19-BLUE property
5.20-Gradient descent
5.21-Gradient descent continued
5.22- Example for gradient descent method
5.23-Linear regression example
5.24-Linear regression
5.25-Mathematics behind linear regression
5.26- Linear regression examples
5.27-Linear regression
5.28-Predictions with linear regression
5.29-Preparing data for linear regression
5.30-Non linear regression
5.31-Mathematical approach to non linear regression
5.32-Non linear regression
5.33-Non linear regression algorithm
5.34- Degree of non-linearity for regression
5.35-Training and Test Error
5.36-Bias & Variance- Defined
5.37-Bias vs Variance
5.38-Bias vs Variance-2
5.39- Under & Overfitting
5.40-Training & Test datasets
5.41-Under, over and best fit regression
5.42-Best fit regression
5.43-Under, Best & Overfit- conclusions
5.44-Bias vs Variance Tradeoff
5.45-Regression error
5.46-Regularization
5.47-Types of Regularizations
5.48-Ridge & LASSO regressions
5.49-Ridge vs LASSO regression
5.50-Ridge vs LASSO- Elastic Net
5.51-Piece-wise linear regression
5.52-Decision tree regression
5.53-Decision tree example
5.54-Decision tree building
5.55-Decision trees- Pros and Cons
5.56-Random Forest
5.57-Boosting
5.58-Boosting
5.59-Bagging
5.60-Bagging
5.61-Feature Bagging
Model Building- Classification
5C.1-Introduction
5C.2-Towards logistic regression
5C.3-Logistic regrssion
5C.4-Logistic regression for binary classification
5C.5-Binary classifier as multi-class classifier
5C.6-Classification
5C.7-Binary classifiers
5C.8-k-Nearest neighbour method(kNN)
5C.9-Decision tree classifier
5C.10-Support Vector Machine(SVM)
5C.11-Naive Bayes' classifier
5C.12- Random forest & Multi-class classification
Model Building- Unsupervised Learning
5U.0-Unsupervised learning example
5U.1-Unsupervised learning
5U.2-Unsupervised learning model nuilding
5U.3-Parametric vs Non-parametric
5U.4-Parametric Unsupervised learning
5U.5-Non-parametric Unsupervised learning
5U.6-Clustering
5U.7-Proximity measures
5U.8-Similarity mearures
5U.9-Clustering algorithms types
5U.10-Types of clustering- better explained
5U.11-k-means clustering
5U.12-k-means clustering algorithm
5U.13-k-means clustering explained
5U.14-k-means clustering algorithm explained
5U.15-Hierarchical clustering
5U.16-Parametric clustering
Model Building- Dimensionality Reduction
5DR.0-Introduction
5DR.1-Example for Principal component analysis
5DR.2-Example continued
5DR.3-Principal component analysis(PCA)
5DR.3A-Eigen vectors- Revisited
5DR.4-Principal components
5DR.5-PCA algorithm
5DR.6-PCA-Pros & Cons
5DR.7-When to use PCA?
5DR.8-Singular Value Decomposition(SVD)
5DR.9-SVD explained
5DR.10-Autoencoders
5DR.11-Unsupervised learning- Summary
Machine Building- Reinforcement learning(RL)
5RL.0-Introduction
5RL.1-Supervised vs Unsupervised vs Reinforcement learning
5RL.2-Reinforcement learning
5RL.3-RL concept
5RL.4-Markov Decision Process(MDP)
5RL.5-RL components
5RL.6-Optimality Criterion
5RL.7-RL example
5RL.8- Optimality criterion explained with example
5RL.9-Example continues
5RL.10-Example continued
5RL.11-Example continues
5RL.12-Example continued
5RL.13-Example continues
5RL.14-Brute force method
5RL.15-Monte Carlo method
5RL.16-Monte Carlo method with an example
5RL.17-Monte Carlo method for the previous example
5RL.18-Q-learning
5RL.19-Model Building Conclusions
Model Validation
6.0-Validation in ML
ML-6.0A
ML-6.1
ML-6.2
ML-6.3
ML-6.4
ML-6.5
ML-6.6
ML-6.7
ML-6.8
ML-6.9
ML-6.10
ML-6.11
ML-6.12
ML-6.13
ML-6.14
ML-6.15
ML-6.16
ML-6.17
ML-6.18
ML-6.19
ML-6.20
ML-6.21
ML-6.22
ML-6.23
ML-6.24
ML-6.25
Model Deployment
ML-7.0A
ML-7.0B
ML-7.1
ML-7.2
ML-7.3
ML-7.4
ML-7.5
ML-7.6
Model Testing
ML-8.0
ML-8.1