21-DAYS-PROGRAMMING-CHALLENGE-ACES
@@ Exploring sklearn! @@
Bit intro About library
A python library built upon NumPy ,SciPy and Matplotlib orignal name scikit-learn.
Installation
pip install scikit-learn
Features of Sklearn
- Supervised Learning Model
- Unsupervised learning Model
- Clustering
- Dimenstionality Reduction
- Ensemble Methods
- Feature Extraction
- Feature Selection
- Open Source
π Day 1 :Sklearn Modelling Process:
- Loading ,splitting data
- Training Model
- Model Persistence
- Preprocessing the Dataset(Binarisation,Mean Removal ,Scaling,Noemalisation(L1,L2 normalisation))
π Day 2:Linear Modelling :
- Linear Regression (SL)(Regression) ( logit or MaxEnt Classifier)
π Day 3:Linear Modelling :
- Logistic Regression (SL)(Classification)
- Lasso
- Ridge
- ElasticNet
π Day 4:Gradient Descent Algorithm
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini Batch Gradient Descent
π Day 5:Suppot Vector Machine
- SVM (SL,Classification+Regression)
π Day 6:KNN Algorithm
- KNN as Classifier (SL,Classification+Regression)
- KNN as Regressor
π Day 7:Metrics and scoring
(Not did much read a bit theory)
- Confusion_matrix
- Accuracy
- Precision
- Recall or Sensitivity
- Specificity
π Day 8:PCA
- Incremental PCA (UL + dimensionality Reduction)
- Kernel PCA
π Day 9:Tree
- Decision Tree (ID3 iterative dichotomiser 3)(SL,CART)
- Random Forest
π Day 10:Naive Bais
- Gaussian Naive Bayes (Classification)
π Day 11:Dimension Reduction
- Principal Component Analysis(PCA)
π Day 12:Dimension Reduction
- Singular Vector Decomposition(SVD) [not did much today kam hai kafi!]
π Day 13:Ensemble methods
- Voting Classifier
Soft Voting + with GridSearchCV
π Day 14:Gradient Boosting
Read theory about all
- GBA
π Day 15:DATA PROCESSING
Steps involved in data processing
- Treating up missing values
- Treating outliners
- Dimentionality Reduction
- Variable Transformation and Feature Engineering
π Day 16:Recommender System
- Simple REcommende using IBM formula
π Day 17:Recommender System
- Content based Recommendation(tfid)
π Day 18:Mean shift Clustering Algorithm
π Day 19: Not a good day
- Not having laptop with me
π₯ signed in through phone will read about different types of regression. no code todayπ .
π Day 20: Pipeline
- How to create one and use.
Laptop didn't come today.
Now I am pro at using GitHub on phone.π
π Day 21 Anomaly detection
RESOUCES
Happy to complete this Chanllenge and for sure will continue Learning!