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Machine Learning: Logistic Regression, LDA & K-NN in Python – 100% Free

Coupon Added/Updated On May 15, 2020
Machine Learning: Logistic Regression, LDA & K-NN in Python โ€“ 100% Free

๐Ÿ“– Overview:

Logistic regression in Python. Machine learning models such as Logistic Regression, Discriminant Analysis &KNN in Python

๐Ÿ‘จโ€๐Ÿซ Course Author:

Start-Tech Academy

๐Ÿ“š Requirements:

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

๐Ÿค“ What 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

๐Ÿ“ƒ 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

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

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

    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.

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.

 

Why use Python for Machine Learning?

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

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, itโ€™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 knowledgeโ€”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.

๐Ÿ‘ฅ Who this course is for?

  • 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

 

Enroll now in the Course to get

๐Ÿ… Certificate of Completion
๐Ÿ“น 7.5 hours on-demand video
๐Ÿ“… Full lifetime access to the course

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