Machine Learning and Artificial Intelligence Notes with Example - AGN HUB

Neural Nets and Deep Learning In ML and AI with Examples and Projects 

BigData In ML and AI with Examples and Projects 

Natural Language Processing In ML and AI with Examples and Projects 

Recommender Systems In ML and AI with Examples and Projects 

Principal Component Analysis In ML and AI with Examples and Projects 

K Means Clustering In ML and AI with Examples and Projects 

Support Vector Machine in ML and AI with Exmples and Projects 

Decision Trees and Random Forest in ML and AI with Exmples and Projects 

 KNN in ML and AI with Exmples and Projects

 Logistic Regression in ML and AI with Examples and Projects The Complete Code import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split train = pd.read_csv("titanic_train.csv") def impute_age(cols):     Age = cols[0]     Pclass = cols[1]     if pd.isnull(Age):         if Pclass == 1:             return 37         elif Pclass == 2:             return 29         else:             return 24     else:         return Age train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1) train.drop('Cabin',axis=1,inplace=True) train.dropna(inplace=True) Sex = pd.get_dummies(train['Sex'],drop_first=True) Embark = pd.get_dummies(train['Embarked'],drop_first=True) train = pd.concat([train,Sex,Em...

 Bias Variance Trade off

 Introduction to linear Regression Code python:- Import numpy as np Import pandas as pd Import matplotlib.pyplot as plt Import seaborn as sns Df = pd.read_csv(‘USA_Housing.csv’) Df.head() Df.info()-- total information of dataset Df.describe()-calculated estimation Df.columns-names of columns Sns.pairplot(df) Sns.distplot(df[‘Price’]) Sns.heatmap(df.corr(),annot=True)shows correlation Training a data using Linear regression First of all designing the features We need all the columns name Df.columns Select the names of the columns leaving price and address X = df[[names of columns]] y= df[‘Price’] the labels From sklearn.cross_validation import train_test_split X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4,random_state=101) Test_size is that amount of total data you want to allocate for testset 40% given here Random_state is a number given to randomly split the data Creating and training the model From sklearn.linear_model import LinearRegression Initi...

Introduction to Machine Learning Uses of Machine Learning:- Machine Learning Flow:- Brief Description about Machine Learning:- Supervised Learning:- Unsupervised Learning:- Reinforcement Learning:-