Machine learning with python [Data Science]

Machine learning with python [Data Science], available at $19.99, has an average rating of 5, with 46 lectures, 19 quizzes, based on 1 reviews, and has 11 subscribers.

You will learn about Learn the basics using real world examples. Learn about every major topics ,working with python 3 Understand and learn machine leaning and predictive model based on machine learning. Basics of machine learning on python. Have the skills and understanding of python to confidently apply for machine learning , data science , academics and Python programming jobs. Learn about different types of Machine Learning Agorithms. This course is ideal for individuals who are Those who don't know where to start with python and machine learning. or Those who need a complete guide on how to start and continue their career with python. It is particularly useful for Those who don't know where to start with python and machine learning. or Those who need a complete guide on how to start and continue their career with python.

Enroll now: Machine learning with python [Data Science]

Summary

Title: Machine learning with python [Data Science]

Price: $19.99

Average Rating: 5

Number of Lectures: 46

Number of Quizzes: 19

Number of Published Lectures: 46

Number of Published Quizzes: 19

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 65

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basics using real world examples.
Learn about every major topics ,working with python 3
Understand and learn machine leaning and predictive model based on machine learning.
Basics of machine learning on python.
Have the skills and understanding of python to confidently apply for machine learning , data science , academics and Python programming jobs.
Learn about different types of Machine Learning Agorithms.

Who Should Attend

Those who don't know where to start with python and machine learning.
Those who need a complete guide on how to start and continue their career with python.

Target Audiences

Those who don't know where to start with python and machine learning.
Those who need a complete guide on how to start and continue their career with python.

HERE IS WHY YOU SHOULD TAKE THIS COURSE

This course is complete guide to both Supervised and Unsupervised learning using Python.This means,this course covers all the main aspects of practical Data Science and if you take this course you can do away withtaking other course or buying books on python based Data science .

In this age of Big data companies across the globe use python to sift through the Avalache of information at their disposal..

By becoming proficient in unsupervised and supervised learning in python,you can give your company a competitive edge and boost your careeer to the next level.

LEARN FROM AN EXPERT DATA SCIENCE WITH 3+ YEARS OF EXPERIENCE:

My Name is Aakash Singh and I had also recently published my Research Paper in INTERNATIONAL JOURNAL IJSR on Machine Learning Dataset.

This course will give you robust grounding in the main aspects of Machine Learning-Clustering and Classification.

NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:

you will start by absorbing the most valuable python Data science basics and techniques.

I use easy to understand hands on methods to simplify and address even the most difficult conceptsin python.

My course will help you to implement the methods using real data obtained from different sources.

After using this course you will easily use package like numpy,pandas,and mathplotlib to work with real data in python..

We will go through lab section on jupyter notebook terminal .we will go through lots of real life examples for icreasing practical side knowledge of the programming and we should not neglect theory section als,which is essential for this course,by the end of this course you will be able to code in python language and feel confident with machine learning and you will also be able to create your own program amd implement were you want.

Most importantly ,you will learn to implement these techniques practically using python ,you will have access to all the data and scripts used in this course.remenber ,i am always around to support my students

Both python and machine learning are most neede skills Nowadays

SIGN UP NOW!

Course Curriculum

Chapter 1: Complete Series

Lecture 1: Introduction

Chapter 2: Installation of lab

Lecture 1: jupyter notebook

Chapter 3: Variables of python

Lecture 1: variables of python

Chapter 4: Operators in python

Lecture 1: operators intro

Lecture 2: Arithmetic Operators

Lecture 3: Relational operators

Lecture 4: Assignment Operators

Lecture 5: Logical operators

Lecture 6: Bitwise operator

Chapter 5: Understanding the concepts of loops

Lecture 1: Loops

Chapter 6: Data structure in python

Lecture 1: Introduction to data-structure and numbers

Chapter 7: String

Lecture 1: string

Chapter 8: List

Lecture 1: List

Chapter 9: Tuple

Lecture 1: Tuple

Chapter 10: Dictonary

Lecture 1: Dictonary

Chapter 11: QUIZ FOR DATA STRUCTURE

Chapter 12: Understanding of Functions

Lecture 1: Functions Intro

Lecture 2: Functions Code

Chapter 13: Modules of python

Lecture 1: Modules intro

Lecture 2: Modules code

Chapter 14: Exceptional Handling

Lecture 1: Exceptional Handling intro

Lecture 2: Exceptional Handling code

Chapter 15: File handling

Lecture 1: File handling Intro

Lecture 2: File handling code

Chapter 16: oop concept

Lecture 1: oop concept Intro

Lecture 2: oop concept Code 1

Lecture 3: oop concept Code 2

Lecture 4: Inheritance

Chapter 17: Project 1

Lecture 1: Project 1

Chapter 18: HOW MACHINE LEARNS?

Lecture 1: How Machine learns?

Chapter 19: Introduction to pandas

Lecture 1: pandas

Chapter 20: Data visualization

Lecture 1: Data visualization

Chapter 21: Data Pre-processing

Lecture 1: Data Pre-processing theory

Lecture 2: Data Pre-processing code

Chapter 22: Linear Regression

Lecture 1: Linear Regression theory

Lecture 2: Linear Regression code

Chapter 23: Logistic Regression

Lecture 1: Logistic Regression theory

Lecture 2: Logistic Regression code

Chapter 24: KNN

Lecture 1: KNN theory

Lecture 2: KNN code

Chapter 25: Decision Tree

Lecture 1: Decision Tree Theory

Lecture 2: Decision Tree code

Chapter 26: Naive Bayes

Lecture 1: Naive Bayes theory

Lecture 2: Naive Bayes code

Chapter 27: Random Forest

Lecture 1: Random Forest thoery

Lecture 2: Random Forest code

Chapter 28: Project 2

Lecture 1: Project 2

Instructors

Aakash Singh
Software Engineer

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5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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