Machine Learning with Python

Course Duration: 120h

Course level:All Levels

About Course

Machine learning helps computers act without explicitly programmed. Machine learning has helped us develop self-driving cars, speech recognition algorithms, and other useful applications that we use them dozens of times a day without knowing them. In this course, you will learn the most effective machine learning techniques. You will get opportunity to implement them through hands-on projects and get them to work for yourself. In this course, you will not only learn the theoretical underpinnings of learning but also learn to apply the techniques to solve new problems. Today, machine learning is one of the most in-demand skills for job. The hiring in the machine learning field has grown 74% annually for the last four years. This course will help all those who are interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning and Supervised Learning.

Course Curriculum

Introduction to Machine Learning
Machine Learning Project Lifecycle
Project - 1 - Finance
Function Approximation
Prediction Accuracy vs Model Interpretability
Assessing Model Accuracy
Bias Variance Trade Off
Project - 2 - Healthcare
Supervised Learning - Regression
Regression - Accuracy of Coefficient Estimate and Model
Project - 3 - Marketing
Supervised Learning - Classification
Logistic Regression and LDA
Project - 4 - Finance
Resampling Methods
Shrinkage Methods
Dimension Reduction Methods
Project 6 - HR
Ensemble Methods - Decision Tree
Bagging, Boosting, Random Forest
Project 7 - Compensation and Benefits
Support Vector Machines
Project 8 - SVM
Unsupervised Learning - PCA
Unsupervised Learning - Clustering
Project 9 - PCA
Reinforcement Learning - Hidden Markov Model

Course in the Hindi Language
100% online course
Hands-on Projects
Earn a sharable certificate upon completion
Affordable Program
Complete the course according to your schedule
Get tangible career benefits after completion of the course
Approximately 55 hours of course content
No hard prerequisites

Course Curriculum

Machine Learning

Learning Systems

11:29
Input – Output Variables

05:24
Independent vs Dependent Variable

05:59

Machine Learning Project Lifecycle

Machine Learning Project Lifecycle

04:28
Six Steps Introduction

12:46
Benefits of Structured Approach

01:59
Agile Methodology

01:43
Step 1 – Define Research Goals

08:37
Step 1 – Goal and Scope of the Project

04:19
Step 1 – Project Charter

02:32
Step 2 – Data Retrieval

03:10
Step 2 – Data Retrieval – Acquire Internal Data

06:41
Step 2 – Data Retrieval – Acquire External Data

03:22
Step 2 – Data Retrieval – Check the Data Quality at Source

07:00
Step 2 – Data Retrieval – Cleansing, Integrating, Transforming Data

02:44
Step 3 – Data Preparation – Cleansing, Integrating, Transforming Data

02:44
Step 3 – Data Preparation – Subprocesses

01:05
Step 3.1 – Cleansing Data

04:17
Step 3.1 – Cleansing Data – Data Entry Errors

02:19
Step 3.1 – Cleansing Data – Redundant Whitespace

02:39
Step 3.1 – Cleansing Data – Capital Letter Mismatch

01:01
Step 3.1 – Cleansing Data – Impossible Values

00:52
Step 3.1 – Cleansing Data – Outliers

03:57
Step 3.1 – Cleansing Data – Missing Values

04:49
Step 3.1 – Cleansing Data – Unit of Measurement

01:30
Step 3.1 – Cleansing Data – Correct Error as Early as Possible

08:35
Step 3.2 – Integrating Data – Combine Data from Different Sources

02:58
Step 3.2 – Integrating Data – Joining Tables

02:30
Step 3.2 – Integrating Data – Appending Table

01:11
Step 3.2 – Integrating Data – Using Views

02:30
Step 3.3 – Transforming Data

02:29
Step 3.3 – Transforming Data – Reduce Number of Variables

03:35
Step 3.3 – Transforming Data – Dummy Variables

02:39
Step 4: Exploratory Data Analysis

01:40
Step 4: EDA

03:42
Step 5 – Data Modeling

02:40
Step 5 – Data Modeling – Model and Variable Selection

03:22
Step 5 – Data Modeling – Model Execution

04:24
Step 5 – Data Modeling – Result Analysis – Model Fit

06:02
Step 5 – Data Modeling – Predictor Coefficient

03:54
Step 5 – Data Modeling – Predictor Significance

02:09
Step 5 – Data Modeling – k-nearest Neighbour

06:54
Step 5 – Data Modeling – Model Diagnostic and Comparison

06:00
Present Finding and Build Applications

01:23

Project 1 – Finance – Fraudulent Transaction

Introduction

09:41
Problem Statement

05:12
Python Package

03:20
Import Python Packages

03:09
Understand the Data

08:53
Exploratory Data Analysis

10:06
Train-Test Split

08:48
Data Modeling

10:38
Model Selection

02:16
Confusion Matrix

07:34

Function Approximation

Prediction Accuracy and Interpretability of Model

Model Accuracy

Bias Variance Tradeoff

Approaches For Prediction

Project 2 – Healthcare – Predict Diabetes

Linear Regression

Introduction

01:53
Important Point about Linear Regression

03:11
Simple Linear Regression

04:51
Coefficients

14:15
Population Regression Line

08:07
Standard Error

08:07
Residual Standard Error

04:40
Confidence Interval

01:34
Hypothesis Test

02:27
Residual Standard Error

03:38
R Squared Statistics

05:33

Project 3 – Manufacturing

Functional vs Statistical Relationship

05:24
Project Statement

04:12
Analysis of Data

15:06
Graphical Representation

10:39
Method of Least Square

05:59
Trial and Error Method

07:42
Least Square Estimator

00:00
Residuals

03:49
Properties of Fitted Regression Line

03:48
Regression Summary Output

10:42
ANOVA – Residuals

04:33
ANOVA – Regression

08:52
Analysis of Variance Table

07:10
Accessing Model Accuracy

01:15
Residual Standard Error

02:42
R-Sq Coefficient of Determination

06:03
Multiple r – Coefficient of Correlation

02:23
Adjusted R-Sq

04:26

Project 4 – Marketing

Objective

05:07
Research Goals and Data

01:37
Import Libraries and Upload Data

01:53
Understanding Data

05:53
Data Analysis

05:25
Relationship – Predictor and Response

04:10
Data Normalization

03:19
Linear Regression (Scaled Data)

03:34
Linear Regression (Unscaled Data)

04:21
Regression using Statsmodel

06:52
Single Linear Regression

03:29
Multiple Linear Regression

04:41
Correlation Matrix

03:04

Classification

Logistic Regression

Linear vs Logistic Regression

10:28
Logistic Regression

03:58
Logistic Model

08:60
Estimate Coefficients

00:00
Analysis of Results

06:22
Making Predictions

03:25
Dummy variable approach

05:04
Multiple Logistic Regression

12:43

Linear Discriminant Analysis

Logistics Regression for more than 2 Output Class

00:00
Linear Discriminant Analysis

02:37
Logistic Regression vs LDA

01:52
Bayes’ Theorem

13:49
LDA Single Predictor

00:00
LDA more than 1 Predictor

05:35
LDA Case Study

15:23
ROC Curve

09:34
QDA

02:49

Project – 5 – Finance

Problem Statement

03:39
Load Data and Analysis

03:39
Exploratory Data Analysis

00:00
Graphical Representation

00:00
Logistic Regression – Sklearn

00:00
Logistic Regression – statsmodel

03:30
Logistic Regression – Dummy Variable

02:37
Multiple Logistic Regression

01:17
Cofounding

00:00
Linear Discriminant Analysis

00:00

₹2,500.00

Machine Learning with Python

About Course

Course Curriculum

What I will learn?

Course Curriculum

Machine Learning

Learning Systems

Input – Output Variables

Independent vs Dependent Variable

Machine Learning Project Lifecycle

Machine Learning Project Lifecycle

Six Steps Introduction

Benefits of Structured Approach

Agile Methodology

Step 1 – Define Research Goals

Step 1 – Goal and Scope of the Project

Step 1 – Project Charter

Step 2 – Data Retrieval

Step 2 – Data Retrieval – Acquire Internal Data

Step 2 – Data Retrieval – Acquire External Data

Step 2 – Data Retrieval – Check the Data Quality at Source

Step 2 – Data Retrieval – Cleansing, Integrating, Transforming Data

Step 3 – Data Preparation – Cleansing, Integrating, Transforming Data

Step 3 – Data Preparation – Subprocesses

Step 3.1 – Cleansing Data

Step 3.1 – Cleansing Data – Data Entry Errors

Step 3.1 – Cleansing Data – Redundant Whitespace

Step 3.1 – Cleansing Data – Capital Letter Mismatch

Step 3.1 – Cleansing Data – Impossible Values

Step 3.1 – Cleansing Data – Outliers

Step 3.1 – Cleansing Data – Missing Values

Step 3.1 – Cleansing Data – Unit of Measurement

Step 3.1 – Cleansing Data – Correct Error as Early as Possible

Step 3.2 – Integrating Data – Combine Data from Different Sources

Step 3.2 – Integrating Data – Joining Tables

Step 3.2 – Integrating Data – Appending Table

Step 3.2 – Integrating Data – Using Views

Step 3.3 – Transforming Data

Step 3.3 – Transforming Data – Reduce Number of Variables

Step 3.3 – Transforming Data – Dummy Variables

Step 4: Exploratory Data Analysis

Step 4: EDA

Step 5 – Data Modeling

Step 5 – Data Modeling – Model and Variable Selection

Step 5 – Data Modeling – Model Execution

Step 5 – Data Modeling – Result Analysis – Model Fit

Step 5 – Data Modeling – Predictor Coefficient

Step 5 – Data Modeling – Predictor Significance

Step 5 – Data Modeling – k-nearest Neighbour

Step 5 – Data Modeling – Model Diagnostic and Comparison

Present Finding and Build Applications

Project 1 – Finance – Fraudulent Transaction

Introduction

Problem Statement

Python Package

Import Python Packages

Understand the Data

Exploratory Data Analysis

Train-Test Split

Data Modeling

Model Selection

Confusion Matrix

Function Approximation

Function f

Approximate Function f

Reducible and Irreducible Errors

Inference

Prediction Accuracy and Interpretability of Model

Trade-Off Between Prediction Accuracy and Model Interpretability

Model Accuracy

No Free Lunch Theorem

Measure Quality of Fit

Training vs Test MSE

Model Selection

Model Selection – II

Bias Variance Tradeoff

Bias Variance

Bias Variance Meaning

Bias Variance Tradeoff

Approaches For Prediction