Machine Learning with Python

Machine Learning with Python, available at $19.99, has an average rating of 3.88, with 72 lectures, based on 4 reviews, and has 1220 subscribers.

You will learn about Make predictions using linear regression, polynomial regression, and multivariate regression Master Machine Learning on Python Make robust Machine Learning models Have a great intuition of many Machine Learning models This course is ideal for individuals who are Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools It is particularly useful for Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools.

Enroll now: Machine Learning with Python

Summary

Title: Machine Learning with Python

Price: $19.99

Average Rating: 3.88

Number of Lectures: 72

Number of Published Lectures: 72

Number of Curriculum Items: 72

Number of Published Curriculum Objects: 72

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Make predictions using linear regression, polynomial regression, and multivariate regression

Master Machine Learning on Python

Make robust Machine Learning models

Have a great intuition of many Machine Learning models

Who Should Attend

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who want to create added value to their business by using powerful Machine Learning tools

Target Audiences

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who want to create added value to their business by using powerful Machine Learning tools

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

What is Machine learning

Features of Machine Learning

Difference between regular program and machine learning program

Applications of Machine Learning

Types of Machine Learning

What is Supervised Learning

What is Reinforcement Learning

What is Neighbours algorithm

K Nearest Neighbours classification

K Nearest Neighbours Regression

Detailed Supervised Learning

Supervised Learning Algorithms

Linear Regression

Use Case(with Demo)

Model Fitting

Need for Logistic Regression

What is Logistic Regression?

Ridge and lasso regression

Support vector Machines

Pre process of Machine learning data

ML Pipeline

What is Unsupervised Learning

What is Clustering

Types of Clustering

Tree Based Modeles

What is Decision Tree

What is Random Forest

What is Adaboost

What is Gradient boosting

stochastic gradient boostinng

What is Na?ve Bayes

Calculation using weather dataset

Entropy Calculation using weather dataset

Trees Entropy and Gini Maths Introduction

Pipeline with SimpleImputer and SVC

Pipeline with feature selection and SVC

Dropping Missing Data

Regression with categorical features using ridge algorithm

processing Categorical Features part2

processing Categorical Features

processing of machine learning data Delete Outliers

processing of machine learning data Outliers

Course Curriculum

Chapter 1: Machine Learning Introduction

Lecture 1: ML01_01_Machine Learning Introduction and Defination

Lecture 2: Ml02_01_ETP_Defimation

Lecture 3: ML03_01_Applications of ML

Lecture 4: ML04_01_Types of Machine Learning and Supervised Learning Introduction

Lecture 5: ML05_01_UnSupervised Learning Introduction

Lecture 6: ML06_01_reading _sklearn_ml_package_help_document part 1

Lecture 7: ML07_01_reading _sklearn_ml_package_help_document part 2

Lecture 8: ML08_01_Test Your Understanding

Chapter 2: Working with Datasets

Lecture 1: ML09_02_Explore Toy-Datasets

Lecture 2: ML10_02_Explore iris Dataset

Lecture 3: ML11_02_Similarly explore remaining toy datasets

Lecture 4: ML12_02_Create DataFrame from sklearn Bunch

Lecture 5: ML13_02_Create a Bunch with our own data

Lecture 6: ML14_02_Create a Bunch with our own data part 2

Chapter 3: k nearest neighbor algorithm

Lecture 1: ML15_03_k nearest neighbor algorithm Maths

Lecture 2: ML16_03_Find unknown sample quality based on known samples

Lecture 3: ML17_03_Find unknown flower name based on known flower names using MS excel

Lecture 4: ML18_03_Importance of n_neighbors

Lecture 5: ML19_03_Hamming distance

Chapter 4: KNN Estimator from Scratch

Lecture 1: ML20_04_KNN Estimator from Scratch

Lecture 2: ML21_04_Write code to Locate the most similar neighbors

Lecture 3: ML22_04_Write code to Make a classification prediction with neighbors

Lecture 4: ML23_04_High level End to End ML project Steps

Lecture 5: ML24_04_Load csv file and Understand X and y Data

Lecture 6: ML25_04_Split Data for training and testing

Lecture 7: ML26_04_Train or fit the model

Lecture 8: ML27_04_Predict labels of test data

Lecture 9: ML28_04_Accuracy_of_the_Clasification_model

Lecture 10: ML29_04_Hyper_Parameter_tunning

Lecture 11: ML30_04_k means cross validation

Lecture 12: ML31_04_GridSearchCV Hyper Parameter Tunning

Lecture 13: ML32_04_RandomizedSearchCV Hyper Parameter Tunning

Lecture 14: ML33_04_Save The model

Lecture 15: ML34_04_Load The model

Lecture 16: ML35_04_Home_Work

Chapter 5: Linear Regression

Lecture 1: ML36_05_Linear Regression Maths

Lecture 2: ML37_05_Find weight of the baby based on age data understanding

Lecture 3: ML38_05_Ordinary Least Squares

Lecture 4: ML39_05_Find parameters using Ordinary Least Squares Function

Lecture 5: ML40_05_Find parameters using sklearn

Lecture 6: ML42_05_Find parameters using covar and var

Lecture 7: ML43_05_Multivariate Linear Regression

Lecture 8: ML44_05_Linear_regression_to find life span based on number of fertilities part

Lecture 9: ML45_05_Linear_regression_to find life span based on number of fertilities part

Lecture 10: ML46_05_Supervised_Regression_Metric_R2_score

Lecture 11: ML47_05_Supervised_Regression_Metrics_RMSE

Lecture 12: ML48_05_Life Span Predication

Lecture 13: ML49_05_Linear Regression with Cross Validation or K-Fold

Lecture 14: ML50_05_Linear Regression with Boston dataset

Lecture 15: ML52_06_Logistic Regression Binary Clasification

Chapter 6: ML51_06_Logistic Regression Maths

Lecture 1: ML51_06_Logistic Regression Maths

Lecture 2: ML52_06_Logistic Regression Binary Clasification

Lecture 3: ML_53_06_Confusion Matrix

Lecture 4: ML_54_06_Classification Report

Lecture 5: ML_55_06_ROC Curve

Lecture 6: ML_56_06_AUC Computation

Chapter 7: Support Vector Machines Introduction

Lecture 1: ML_57_07_Support Vector Machines Introduction

Lecture 2: ML_58_07_Support Vectors and Maximizing the Margin

Lecture 3: ML_59_07_Non_linear_Support Vectors and Maximizing the Margin

Lecture 4: ML_60_07_upport Vector Machines Using Iris Toy Data set

Lecture 5: ML_61_07_Support_Vector_Machines_for_Face_Recognition

Chapter 8: Pre-processing of machine learning data Outliers

Lecture 1: ML_62_08_Pre-processing of machine learning data Outliers

Lecture 2: ML_63_08_Pre-processing of machine learning data Delete Outliers

Lecture 3: ML_64_08_Pre-processing Categorical Features

Lecture 4: ML_65_08_Pre-processing Categorical Features part2

Lecture 5: ML_66_08_Regression with categorical features using ridge algorithm

Lecture 6: ML_67_08_Dropping Missing Data

Chapter 9: ML_Pipeline with feature_selection and SVC

Lecture 1: ML_68_09_ML_Pipeline with feature_selection and SVC

Lecture 2: ML_69_09_ML_Pipeline with SimpleImputer and SVC

Chapter 10: Trees Entropy and Gini Maths Introduction

Lecture 1: ML_70_10_Trees Entropy and Gini Maths Introduction

Lecture 2: ML_73_10_Entropy Calculation using weather dataset part 3

Lecture 3: ML_74_10_Entropy Calculation using weather dataset part 4

Instructors

Ram Reddy
Data Scientist

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4 stars: 2 votes

5 stars: 1 votes

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