Python for Data Science and Machine Learning

A Practical Approach Learn by Implementing

3.70 (130 reviews)
Udemy
platform
English
language
IT Certification
category
18,144
students
7 hours
content
Jun 2021
last update
$19.99
regular price

What you will learn

Downloading and Installing the required Files for Data Science and Machine learning

All about Numpy : array manipulation , slicing , transpose , and all functions of it etc..

How to Use Python for Data Science and Machine Learning

Learn to use Pandas for Data Analysis

A-Z pandas

Exploratory Data Analysis

Problem Solving Skills based on Data Science and Ml problems.

Statistics Required For Data Science and Machine Learning

Stock Data Analysis

Working with Different Datasets and Making Predictions

Applying Decision tree classifier , Naive bayes and other Machine Learning Models to solve the problems

Understanding Regression and Correlation

Building Regression Model to deal with house price prediction

Text Data Extraction from Image

Kmeans and Hierarchical Clustering etc..

Description

This course is based on practical Approach towards Machine Learning and Data Science.

Starting from the basic python libraries and going to implement and perform more complex level predictions.

There is no prerequisite for this course but still you must go through the python basic documentation which you will get in this course material.

and you must have basic knowledge of python before starting with this course.

There will be some assignments based on data science which will include

  • stock analysis

  • text extraction

  • working on different data sets

  • applying different algorithms in an effective manner.

  • more

Here we explore different methods , libraries . make predictions , verify results and more .

Learn how to use

  • NumPy

  • Pandas

  • Seaborn

  • Matplotlib

  • Plotly

  • Scikit-Learn

  • Machine Learning

  • more!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This will going to most effective and comprehensive course for data science aspirants

you will be able to think like a data wizard or data scientist.

you will get the dataset from the course material and also its available on web i will show you have to download that also.

you will also get some good understanding of

data cleaning

data preprocessing

data methodologies

applying tests

etc

updates

CODES ALL UP TO DATE


Who this course is for:

  • Anyone interested in Machine Learning , Data Science.

  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.

  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

  • Any students in college who want to start a career in Data Science.

  • Any data analysts who want to level up in Machine Learning.

  • Any people who are not satisfied with their job and who want to become a Data Scientist.

  • Any people who want to create added value to their business by using powerful Machine Learning tools.

  • Students in the field of Data Science

  • Any one who is willing to learn can join .


Stay tuned I will continue bringing more such courses to stay productive and updated with latest trends

in Computer Science Engineering and Data Science.

Also please provide your Wonderful Feedback and Rating.


Thank you ...

In this hard times also continue to upgrade yourself and be trained for the future

Stay safe and stay home.



Content

Introduction

Downloading and Installing the required Files

Numpy : numpy functions , Array operations , more

Numpy : Numpy functions , Array operations , more
Numpy Part 2

A-Z Pandas

Pandas Imp Doc
Pandas theory
Pandas part 1
Pandas part 2

Statistics For Data Science

Statistics For Data Science and ML

Stock Data Analysis

Stock Data Analysis

Working with Datasets and Making Predictions

Titanic Dataset Predictions
Weather Data Analysis
Naive Bayes Classifier
Applying Naive Bayes classifier on Iris Data
Naive Bayes Classifier Iris Data Part 2
Regression (Linear and Multiple Regression)
Understanding Regression and Correlation and Implementing T statistic
House data Prediction and Analysis using Linear Regression

Data Extraction from Image

Text Extraction from Image

Kmeans and Hierarchical Clustering

Kmeans and Hierarchical

Screenshots

Python for Data Science and Machine Learning - Screenshot_01Python for Data Science and Machine Learning - Screenshot_02Python for Data Science and Machine Learning - Screenshot_03Python for Data Science and Machine Learning - Screenshot_04

Reviews

Albert
June 19, 2021
good hands on course best in data science. will recommend this to everyone whether a beginner or expert.
Nancy
June 19, 2021
This Course was really outstanding from beginning to the end and also their were lots of hands on labs and explanation was fantastic Enjoyed a lot thank you.
Ude
June 16, 2021
Half the time he is inaudible. He just types codes and does not properly explain the code. I am not getting value for my money because I have to study other materials to understand half of what he is saying.
Noah
May 9, 2021
Best Course in the field of Data Science and Machine learning , I learnt to implement different algorithms and find meaningful insights by doing data prediction and analysis , and also was able to understand the accent and would recommend everyone to join this. thanks a lot.
Drishti
April 14, 2021
The course is good upto section 3 i.e. A-Z pandas. Instructor explains everything well upto section3 . But after that he is only writing codes and not explaining the work of functions/codes used.You will have to google every function used to know its concept and syntax.
Akshar
April 12, 2021
Could dive deep into the interpretations of the plots and the methods in the last few modules, especially the KMeans.

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3939442
udemy ID
3/26/2021
course created date
3/31/2021
course indexed date
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