- Definition of Machine Learning
- Application of Machine Learning
- Installation of Anaconda Python
- Installation of R and RStudio IDE
- Basics of Data pre processing
- Importing libraries in Python
- Importing Data Sets in R and Python
- Data Pre-processing: Handling missing Data in Python and R
- Data Pre-processing: Encoding categorical data in Python and R
- Write up for entire Data-preprocessing till now
- Splitting Data set into train data and test data
- Feature Scaling
- Writeups for Splitting Data Sets and Feature Scaling
- Wrapping up Data-Preprocessing part
- Introduction to Simple Linear Introduction
- Simple Linear Regression Code in Python
- Simple Linear Regression Code in R Programming
- Write up for Simple Linear Regression with explanation of coding in both R and Python
- Introduction to Multi Linear Regression
- Multi Linear Regression code in Python with writup
- Multi Linear Regression code in R Programming
- Multi Linear Regression conclusion and writeup for both R and Python programming
- Introduction to Polynomial Regression
- Stepwise Polynomial Regression Code in Python and its writeup
- Stepwise Polynomial Regression Code in R programming and its writeup
- Introduction to Support Vector Regression
- Support Vector Regression Code in Python
- Support Vector Regression Code in R Programming
- Introduction to Decision Tree Regression
- Decision Tree Regression in Python
- Decision Tree Regression in R Programming
- Introduction to Random Forest Regression
- Random Forest Regression in Python
- Random Forest Regression In R
- Concluding Regression
- Comparing Different Regression Models
- R Squared Approach
- Adjusted R Squared Approach
- Introduction to Classifications
- Introduction to Logistic Regression
- Logistic Regression in Python
- Logistic Regression in R
- Introduction To K-Nearest Neighbor Classification
- Classification of data set using K-Nearest Neigbors in Python
- Classification of data set using K-Nearest Neigbors in R
- Introduction to classification using Support Vector Machines.
- Support Vector Machine Classification in Python
- Support Vector Machine Classification in R Programming
These days are clubbed together because I had to spend full time in travel and industrial visit at Xebia Gurugram, India office.
- Introduction to Kernel SVM and programmed them in Python and R
- Introduction to Classification using Naive Bayes Algorithm.
- Data Classification using Naive Bayes method in Python
- Data Classfication using Naive Bayes method in R
- Introduction to Decision Tree Classification
- Decision Tree Classification in Python
- Decision Tree Classification in R
- Introduction to Random Forest Classification
- Random Forest Classification in Python
- Random Forest Classification in R
- Wrapping up Data classification
- Introduction to Clustering Algorithms
- Introduction to K-Means clustering
- K-Means clustering in Python
- K-Means clustering in R
- Introduction to Hierarchical Clustering
- Hierarchical Clustering in Python
- Hierarichal Clustering in R
- Comparison between different clustering algorithms
- Introduction to Association Rule Learning
- Apriori Rule in R
- Introduction to Apriori Rule in Python and template file
- Apriori Rule in Python
- Association Rule Learning with Eclat
- Introduction to Reinforcement Learning
- Introduction to Upper Confidence Bounds Algorithm
- Reinforcement Learning using Random Selections in both R and Python
- Upper Confidence Bound algorithms in R
- Upper Confidence Bound algorithms in Python
- Introduction to Thompson Sampling
- Thompson Sampling in Python
- Thompson Sampling in R
- Introduction to Natural Language Processing
- NLP programming in Python
- NLP programming in R
- Introduction to Deep Learning
- Introduction to Artificial Neural Networks
- Artificial Neural Networks in Python
- Artificial Neural Networks in R
- Introduction to Convolutional Neural Networks
- Convolutional Neural Networks in Python
- Introduction to Dimensionality Reduction
- Introduction to Principal Component Analysis (PCA)
- Principal Component Analysis in Python
- Principal Component Analysis in R
- Introduction to Linear Discriminant Analysis
- Linear Discriminant Analysis in Python
- Linear Discriminant Analysis in R
- Introduction to Kernel PCA
- Kernel PCA in Python
- Kernel PCA in R
- Introduction to Model Selection
- Introduction to Grid Selection and programming it in Python
- Grid Selection programmin in R
- K-fold Cross Validation in Python
- K-fold Cross Validation in R
- Introduction to XGBoost
- XGBoost programming in Python
- XGBoost programming in R
- Introduction to AIOps
- AIOps with MoogSoft
- AIOps with DataDog
- Data Visualization | Tableau
- Data Visulization | Power BI
- Data Visualization | Grafana
- Natural Language Processing | AWS Comprehend
- AWS Comprehend | Day 2
- Implementing Real Time Social Media mining project | Sentimental Analysis
- Starting my journey to Machine Learning on cloud. Follow my project here.