In this repository, I will upload my 100 Days ML Code which I have learned from different courses(Upgrad,Coursera, udemy, edx, udacity), different websites blogs, different tutorials from YouTube, books, and research papers. From 01-June-2022 till ####
Machine Learning Deep Learning Computer Vision Natural Language Processing
Day 1: June 1st , 2022 ->
Today's Progress : Learn basics of Numpy.- Basics of Numpy
Day 2: June 2nd , 2022 ->
Today's Progress : Linear Regression with Notes
Cost Function of Linear Regression
Day 3: June 3rd, 2022 ->
Today's Progress : Logistic Regression with Notes Cost Function
Day 4: June 4th, 2022 ->
Today's Progress : Pandas 100 Common Commands Huge List Dont memorise keep the Excel Handy
Day 5: June 5th, 2022 ->
Today's Progress : Understanding the Lasso(L1) and Ridge(L2) Regression.
Day 6: June 6th, 2022 ->
Today's Progress : Understanding Accuracy,Precession,Recall Let's Assume I have a trained a bot to allow only Dogs to Dog show which is standing near the entrence of the event.
Accuracy:- Accuracy says how much my bot is capable of allowing if any new dogs come to show. As per the below result new dog as 50% chance to make it to the Dog show.
Precision:- Precision is out of all Dog prediction how much our bot was capable of predicting, In the below case it is 57% means which means what ever has made to Dog show only 57% are dog rest is something else(even house,bird and car has made it to the show)
Recall:- Out of all dog truth how many we got it right. Our bot couldnot allow 2 dogs even though our dogs were pefect but bot missclassified then as non dog so 67% time if any
new dog comes chances are thre it make it to the show.
Day 7: June 7th, 2022 ->
Today's Progress : Understanding Confusion Matrix
Day 8: June 8th, 2022 ->
Today's Progress : Understanding ROC curve
Day 9: June 9th, 2022 ->
Today's Progress : Decision Tree
Day 10: June 10th, 2022 ->
Today's Progress : Information Gain
Day 11: June 11th, 2022 ->
Today's Progress : Gini Impurity Defination & Calculation
Day 12: June 12th, 2022 ->
Today's Progress : Information Gain, Entropy & Gini Computation in Python
Day 13: June 13th, 2022 ->
Today's Progress : Decision Tree Algorithm Decision Tree Algorithm Details
Day 14: June 14th, 2022 ->
Today's Progress : Ensemble Methods
Day 15: June 15th, 2022 ->
Today's Progress : Time series forcasting explained
Day 16: June 16th, 2022 ->
Today's Progress : Principal component analysis(PCA)
image credit-devopedia
Day 17: June 17th, 2022 ->
Today's Progress : FeatureEngineering & Cross Validation Techniques
Day 18: June 18th, 2022 ->
Today's Progress : Loss Function
Day 19: June 19th, 2022 ->
Today's Progress : Handle Imbalanced Data
Day 20: June 20th, 2022 ->
Today's Progress : Outliers Detections and Treatment
Day 21: June 21st, 2022 ->
Today's Progress : Basics of NLP NLP- Tokenizing Sentences, Tokenizing words, Stemming ,Lemmatization ,Bag of words (BOW),TF-IDF , Classifying the message using Bag of words & TFIDF technique & Word2Vec
Day 22: June 22nd, 2022 ->
Today's Progress : Support vector Machine(SVM) & Principal Component Analysis (PCA)
Day 23: June 23rd, 2022 ->
Today's Progress : GridSearchCV
When it comes to iteratively building machine learning models, GridSearchCv is one of your best friends. It is a class object from the sklearn.model_selection library that condenses what would be the tedious process that is tuning the hyperparameters of a machine learning model into just a few lines of code. Refer:- https://medium.com/@ericdnbn/the-gift-that-is-gridsearchcv-a-tutorial-e2080f03b62d
Day 24: June 24th, 2022 ->
Today's Progress : Market Basket analysis ,Building Recommendation system
Day 25: June 25th, 2022 ->
Today's Progress : Statistics
Day 26: June 26th, 2022 ->
Today's Progress : Statistics-Hypothesis Testing [One sample t-test, Chi square test, Correlation,ANNOVA Test, Two sample T test , Paired sample T test]
Day 27: June 27th, 2022 ->
Today's Progress : Bayes Theorem Bayes Formula:-
Example:-
Day 28: June 28th, 2022 ->
Today's Progress : NLP Different Vectoriser Methods
