4.29 (676 reviews)

$19.99

Regular PriceWhat you will learn

☑ Understand how to interpret the result of Logistic Regression model in Python and translate them into actionable insight

☑ Learn the linear discriminant analysis and K-Nearest Neighbors technique in Python

☑ Preliminary analysis of data using Univariate analysis before running classification model

☑ Predict future outcomes basis past data by implementing Machine Learning algorithm

☑ Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem

☑ Learn how to solve real life problem using the different classification techniques

☑ Course contains a end-to-end DIY project to implement your learnings from the lectures

☑ Basic statistics using Numpy library in Python

☑ Data representation using Seaborn library in Python

☑ Classification techniques of Machine Learning using Scikit Learn and Statsmodel libraries of Python

Description

You're looking for a complete** Classification modeling course** that teaches you everything you need to create a Classification model in Python, right?

**You've found the right Classification modeling course!**

After completing this course **you will be able to**:

Identify the business problem which can be solved using Classification modeling techniques of Machine Learning.

Create different Classification modelling model in Python and compare their performance.

Confidently practice, discuss and understand Machine Learning concepts

**How this course will help you?**

A **Verifiable Certificate of Completion** is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN

**Why should you choose this course?**

This course covers all the steps that one should take while solving a business problem using classification techniques.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

**What makes us qualified to teach you?**

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

*This is very good, i love the fact the all explanation given can be understood by a layman - Joshua*

*Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy*

**Our Promise**

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

**Download Practice files, take Quizzes, and complete Assignments**

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

**What is covered in this course? **

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

**Section 1 - Basics of Statistics**This section is divided into five different lectures starting from types of data then types of statistics

then graphical representations to describe the data and then a lecture on measures of center like mean

median and mode and lastly measures of dispersion like range and standard deviation

**Section 2 - Python basic**This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach

you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

**Section 3 - Introduction to Machine Learning**In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

**Section 4 - Data Pre-processing**In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like

**outlier treatment and missing value imputation.****Section 5 - Classification Models**This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

We have covered the basic theory behind each concept without getting too mathematical about it so that you

understand where the concept is coming from and how it is important. But even if you don't understand

it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a classification model in Python will soar. You'll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems.

**Go ahead and click the enroll button, and I'll see you in lesson 1!**

**Cheers**

**Start-Tech Academy**

------------

Below is a list of popular FAQs of students who want to start their Machine learning journey-

**What is Machine Learning?**

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

**Which all classification techniques are taught in this course?**

In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:

Logistic Regression

Linear Discriminant Analysis

K - Nearest Neighbors (KNN)

**How much time does it take to learn Classification techniques of machine learning?**

Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification.

**What are the steps I should follow to be able to build a Machine Learning model?**

You can divide your learning process into 3 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

**Why use Python for Machine Learning?**

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

**What is the difference between Data Mining, Machine Learning, and Deep Learning?**

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Screenshots

Content

Introduction

Welcome to the course!

Introduction to Machine Learning

Introduction to Machine Learning

Course Resources

Building a Machine Learning model

Basics of Statistics

Types of Data

Types of Statistics

Describing data Graphically

Measures of Centers

Practice Exercise 1

Measures of Dispersion

Practice Exercise 2

Setting up Python and Jupyter Notebook

Installing Python and Anaconda

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Data Preprocessing

Gathering Business Knowledge

Data Exploration

The Dataset and the Data Dictionary

Data Import in Python

Project Exercise 1

Univariate analysis and EDD

EDD in Python

Project Exercise 2

Outlier Treatment

Outlier treatment in Python

Project Exercise 3

Missing Value Imputation

Missing Value Imputation in Python

Project Exercise 4

Seasonality in Data

Variable Transformation

Variable transformation and Deletion in Python

Project Exercise 5

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Project Exercise 6

Classification Models

Three Classifiers and the problem statement

Why can't we use Linear Regression?

Logistic Regression

Training a Simple Logistic Model in Python

Project Exercise 7

Result of Simple Logistic Regression

Logistic with multiple predictors

Training multiple predictor Logistic model in Python

Project Exercise 8

Confusion Matrix

Creating Confusion Matrix in Python

Quiz

Evaluating performance of model

Evaluating model performance in Python

Project Exercise 9

Quiz

Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis

LDA in Python

Project Exercise 10

Test-Train Split

Test-Train Split

Test-Train Split in Python

Project Exercise 11

K-Nearest Neighbors classifier

K-Nearest Neighbors classifier

K-Nearest Neighbors in Python: Part 1

K-Nearest Neighbors in Python: Part 2

Project Exercise 12

Understanding the Results

Understanding the results of classification models

Summary of the three models

The Final Exercise!

Course Conclusion

Course Conclusion

Bonus Lecture

Appendix 1: Linear Regression in Python

The Problem Statement

Basic Equations and Ordinary Least Squares (OLS) method

Assessing accuracy of predicted coefficients

Assessing Model Accuracy: RSE and R squared

Simple Linear Regression in Python

Multiple Linear Regression

The F - statistic

Interpreting results of Categorical variables

Multiple Linear Regression in Python

Reviews

F

Fleming5 December 2020

It is hard for me to understand what the teacher says, his english may be good, but it is difficult for me to hear.

N

Natasha24 October 2020

Yes , I understood what we have done so far etc . Thanks to the teacher with good explanations keep it up !

S

Shule24 October 2020

I can barely understand the lecture because of the accent, so far the lecture is just reading the power point.

G

Giulio18 August 2020

The course is synthetic but quite good, maybe the concepts could be ordered better: the part about the train-test split should be before classification methods. The last part about regressions is missing the pdf files.

P

Praveen10 June 2020

The course is very understandable , the practical part is good . I think the explanation of theory part should be done in a more interactive way . I loved the course till now . very useful . my opinion is that this course would be a wonderful start for aspiring data scientists.

d

daya4 June 2020

A very thanks for the course. Interesting learning experience, course gives the complete understanding of machine learning with its real life application in business.

S

Shobhit27 May 2020

The course was extremely insightful and covered all the topics be it be theoritical or practical with great ease

R

Rodolfo_Rivas15 May 2020

This is a good course to get you up and running with the code. It gives an intuitive introduction to the theory before diving into the code. The course teach more than one tool to predict a binary output (logistic, LDA and K-NN), with the code provided you can run them all and compare the performance among them. This is how things are done in real life. If you know the theory and want to start running these tools in Python it fits the purpose very well. If you don't know the theory, the very brief theoretical introductions give you enough background to interpret the results intuitively.

L

Lewis14 May 2020

Explanations are very good and the courses do not assume advanced levels of understanding before starting the course.

R

Ritik17 April 2020

Content is great but could have shortcuts in a few places. example: creating dummy variables and deletion is shown separately and it is not recommended. instead of a single command of get_dummies wich create dummies and remove firsrt in single command could be told get_dummies(drop_first=true)

J

J-François31 March 2020

Il n'est pas facile de toujours pouvoir suivre l'enseignant à cause de son accent et les traductions automatiques ne correspondent pas toujours. Le curseur et le carré noir se trouvent souvent sur la ligne de code rendant la lecture difficile. C'est mon premier cours sur UDeamy et je suis plutôt satisfait.

J

Juan30 March 2020

Me gustó el curso. Posee información a detalle para el nivel al que está dirigido. Creo que los subtitulos pueden mejorar.

Á

Ángel25 March 2020

Contenido claro, con una gran mezcla entre lo teórico y lo práctico. Muy recomendable para empezar en machine learning

C

Cheecha4 December 2019

I just completed 20% and I must say, It is good. The one thing irritates me is the cursor (Black square). The seasonality calculation example was not properly shown.

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