Course 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

 

Requirements:

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

 

What you'll 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

Course curriculum

  • 1
    Introduction
  • 2
    Basics of Statistics
    • 004 Types of Data
    • 005 Types of Statistics
    • 006 Describing data Graphically
    • 007 Measures of Centers
    • 009 Measures of Dispersion
  • 3
    Python Crash Course
    • 011 Installing Python and Anaconda
    • 012 Opening Jupyter Notebook
    • 013 Introduction to Jupyter
    • 014 Arithmetic operators in Python Python Basics
    • 015 Strings in Python Python Basics
    • 016 Lists Tuples and Directories Python Basics
    • 017 Working with Numpy Library of Python
    • 019 Working with Seaborn Library of Python
    • 018 Working with Pandas Library of Python
  • 4
    Data Preparation and Preprocessing
    • 020 Gathering Business Knowledge
    • 021 Data Exploration
    • 022 The Dataset and the Data Dictionary
    • 023 Data Import in Python
    • 025 Univariate analysis and EDD
    • 026 EDD in Python
    • 028 Outlier Treatment
    • 029 Outlier treatment in Python
    • 031 Missing Value Imputation
    • 032 Missing Value Imputation in Python
    • 034 Seasonality in Data
    • 035 Variable Transformation
    • 036 Variable transformation and Deletion in Python
    • 038 Dummy variable creation Handling qualitative data
    • 039 Dummy variable creation in Python
  • 5
    Classification
    • 041 Three Classifiers and the problem statement
    • 042 Why cant we use Linear Regression
  • 6
    Logistic Regression and LDA
    • 043 Logistic Regression
    • 044 Training a Simple Logistic Model in Python
    • 046 Result of Simple Logistic Regression
    • 047 Logistic with multiple predictors
    • 048 Training multiple predictor Logistic model in Python
    • 050 Confusion Matrix
    • 051 Creating Confusion Matrix in Python
    • 052 Evaluating performance of model
    • 053 Evaluating model performance in Python
    • 055 Linear Discriminant Analysis
    • 056 LDA in Python
    • 058 Test-Train Split
    • 059 Test-Train Split in Python
  • 7
    K-Nearest Neighbors (KNN)
    • 061 K-Nearest Neighbors classifier
    • 062 K-Nearest Neighbors in Python Part 1
    • 063 K-Nearest Neighbors in Python Part 2
  • 8
    Final Discussion
    • 065 Understanding the results of classification models
    • 066 Summary of the three models

Meet your instructor!

Start-Tech Academy
A technology-based analytics education company


Founded by Abhishek Bansal and Pukhraj Parikh, Start-Tech Academy is a technology-based analytics education company and aims at bringing together the analytics companies and interested Learners. 

Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. 

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in  MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

Take this course today!

"Logistic Regression, LDA & KNN in Python - Predictive Modeling"

Bundle including this course!