Machine Learning Basics: Classification models in Python

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    Online

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You're looking for a complete Classification modelling course that teaches you everything you need to create a Classification model in Python, right?You've found the right Classification modelling course!After completing this course you will be able to:Identify the business problem which can be solved using Classification modelling techniques of Machine Learning.
Create different Classification modelling model in Python and compare their performance.
Confidently practice, discuss and understand Machine Learning conceptsHow 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 KNNWhy 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
u will see some examples so that you understand what machine learning actually is. It...

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Online

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About this course

Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
Learn the linear discriminant analysis and K-Nearest Neighbors technique
Learn how to solve real life problem using the different classification techniques
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
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

People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
Anyone curious to master classification machine learning techniques from Beginner to Advanced in short span of time

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Subjects

  • Global
  • Statistics
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Course programme

Introduction 1 lecture 02:52 Welcome to the course! Introduction 1 lecture 02:52 Welcome to the course! Welcome to the course! Welcome to the course! Welcome to the course! Welcome to the course! Introduction to Machine Learning 2 lectures 24:46 Introduction to Machine Learning Building a Machine Learning model Introduction to Machine Learning 2 lectures 24:46 Introduction to Machine Learning Building a Machine Learning model Introduction to Machine Learning Introduction to Machine Learning Introduction to Machine Learning Introduction to Machine Learning Building a Machine Learning model Building a Machine Learning model Building a Machine Learning model Building a Machine Learning model Basics of Statistics 7 lectures 30:10 Types of Data Types of Statistics Describing data Graphically Measures of Centers Practice Exercise 1 Measures of Dispersion Practice Exercise 2 Basics of Statistics 7 lectures 30:10 Types of Data Types of Statistics Describing data Graphically Measures of Centers Practice Exercise 1 Measures of Dispersion Practice Exercise 2 Types of Data Types of Data Types of Data Types of Data Types of Statistics Types of Statistics Types of Statistics Types of Statistics Describing data Graphically Describing data Graphically Describing data Graphically Describing data Graphically Measures of Centers Measures of Centers Measures of Centers Measures of Centers Practice Exercise 1 Practice Exercise 1 Practice Exercise 1 Practice Exercise 1 Measures of Dispersion Measures of Dispersion Measures of Dispersion Measures of Dispersion Practice Exercise 2 Practice Exercise 2 Practice Exercise 2 Practice Exercise 2 Setting up Python and Jupyter Notebook 9 lectures 01:38:02 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 Setting up Python and Jupyter Notebook 9 lectures 01:38:02 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 Installing Python and Anaconda Installing Python and Anaconda Installing Python and Anaconda Installing Python and Anaconda Opening Jupyter Notebook Opening Jupyter Notebook Opening Jupyter Notebook Opening Jupyter Notebook Introduction to Jupyter Introduction to Jupyter Introduction to Jupyter Introduction to Jupyter Arithmetic operators in Python: Python Basics Arithmetic operators in Python: Python Basics Arithmetic operators in Python: Python Basics Arithmetic operators in Python: Python Basics Strings in Python: Python Basics Strings in Python: Python Basics Strings in Python: Python Basics Strings in Python: Python Basics Lists, Tuples and Directories: Python Basics Lists, Tuples and Directories: Python Basics Lists, Tuples and Directories: Python Basics Lists, Tuples and Directories: Python Basics Working with Numpy Library of Python Working with Numpy Library of Python Working with Numpy Library of Python Working with Numpy Library of Python Working with Pandas Library of Python Working with Pandas Library of Python Working with Pandas Library of Python Working with Pandas Library of Python Working with Seaborn Library of Python Working with Seaborn Library of Python Working with Seaborn Library of Python Working with Seaborn Library of Python Data Preprocessing 15 lectures 01:24:46 Gathering Business Knowledge Data Exploration The Dataset and the Data Dictionary Data Import in Python Univariate analysis and EDD EDD in Python Outlier Treatment Outlier Treatment in Python Missing Value Imputation Missing Value Imputation in Python Seasonality in Data Variable Transformation Variable transformation and Deletion in Python Dummy variable creation: Handling qualitative data Dummy variable creation in Python Data Preprocessing trong Logistic with multiple predictors Logistic with multiple predictors Training multiple predictor Logistic model in Python Training multiple predictor Logistic model in Python Training multiple predictor Logistic model in Python Training multiple predictor Logistic model in Python Confusion Matrix Confusion Matrix Confusion Matrix Confusion Matrix Making Confusion Matrix in Python Making Confusion Matrix in Python Making Confusion Matrix in Python Making Confusion Matrix in Python Evaluating performance of model Evaluating performance of model ...

Additional information

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

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Machine Learning Basics: Classification models in Python

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