4.29 out of 5
4.29
314 reviews on Udemy

The Ultimate Data Science & Machine Learning Python in 2019

Numpy, Pandas, Matplotlib, Scikit-Learn, WebScraping, Data Science, cleaning data, Machine Learning, Pyspark, statistics
Instructor:
Ankit Mistry
6,298 students enrolled
English [Auto-generated]
Learn one of the most in demand skill of 21st century Data Science
Update your resume with Data science skills : python, numpy, pandas, plotly, tableau, machine learning, statistics, probability
Apply linear regression and logistics regression on different dataset.
Install anaconda and setup python environment
Python crash course
Numpy - Numerical python
MatplotLIb - Data Visualization library
Pandas - Data analysis library
Plotly Library
Getting start with data visualization tool, Tableau
Data Pre-processing technique - Missing data, Normalization, one hot encoding,
Importing data in Python from different sources, Files
Web Scraping to download web page and extract data
Data scaling and transformation
Exploratory Data analysis
Feature engineering
Machine learning basic theory
Apache spark installation : pyspark
Getting started with spark session
Statistics basics
Basics of Probability
Setup Data Science Virtual machine on Microsoft Azure Cloud

Welcome to Complete Ultimate course guide on Data Science and Machine learning with Python.

Have you ever thought about

How amazon gives you product recommendation, 

How Netflix and YouTube decides which movie or video you should watch next,

Google translate translate one language to another,

How Google knows what is there in your photo,

How  Android speech Recognition or Apple siri understand your speech signal with such high accuracy.

If you would like algorithm or technology running behind that,  This is first course to get started in this direction.

==============================================

This course has more than 100 – 5 star rating.

What previous students have said: 

“This is a truly great course! It covers far more than it’s written in its name: many data science libraries, frameworks, techniques, tips, starting from basics to advanced level topics. Thanks a lot!  “

This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment.”

“learning valuable concepts and feeling great.Thanks for this course.”

Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good”

“i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . it can be improved by including some more examples and real life data but overall i would suggest every beginner to have this course.”

“The instructor is so good, he helps you in all doubts within an average replying time of one hour. The content of the course and the way he delivers is great.”

==================================================

Why Data Science Now?

Data Scientist: The Sexiest Job of the 21st Century – By Harvard Business review

There is huge sortage of data scientist currently software industry is facing.

The average data scientist today earns $130,000 a year  by  glassdoor.

Want to join me for your journey towards becoming Data Scientist, Machine Learning Engineer.

This course has more than 100+ HD –  quality video lectures and is over 13+ hours in content.

This is first introductory course to get started data analysis, Machine learning and towards  AI algorithm implementation

This course will teach you – All Basic python library required for data analysis process.

  • Python  crash course

  • Numerical Python – Numpy

  • Pandas – data analysis

  • Matplotlib for data visualization

  • Plotly and Business intelligence tool Tableau

  • Importing Data in Python from different sources like .csv, .tsv, .json, .html, web rest Facebook API

  • Data Pre-Processing like normalization, train test split, Handling missing data 

  • Web Scraping with python BeautifulSoup – extract  value from structured HTML Data

  • Exploratory data analysis on pima Indian diabetes dataset

  • Visualization of Pima Indian diabetes dataset

  • Data transformation and Scaling Data –  Rescale Data, Standardize Data, Binarize Data, normalise data

  • Basic introduction to What is Machine Learning, and  Scikit learn overview Its type, and comparison with traditional system. Supervised learning vs Unsupervised Learning

  • Understanding of regression, classification and clustering

  • Feature selection and feature elimination technique.

  • And Many Machine learning algorithm yet to come. 

  • Data Science Prerequisite : Basics of Probability and statistics

  • Setup Data Science and Machine learning lab in Microsoft Azure Cloud

This course is for beginner and some experienced programmer who want to make career in Data Science and  Machine learning, AI.

Prerequisite:

  • basic knowledge in python programming

  • High School mathematics

Enroll in this course, take look at brief curriculum of this course and take first step in wonderful world of Data.

See you in field.

Sincerely,

Ankit Mistry

Introduction

1
Download and Install Anaconda - Windows
2
Download and Install Anaconda - Ubuntu Linux
3
Overview Of Jupyter Notebook
4
Notes About Course
5
Introduction

Python crash course

1
Introduction - Python
2
Python - Number, String, Variable
3
Python - List, tuples, Dictionary, Set
4
Python - If/else, Looping
5
Python - Function, Lambda, Map
6
Python
7
Python Exercise
8
Scrap Google home page title.

Data analysis with Numpy

1
Introduction - Numpy - Numerica Python
2
Numpy array
3
Numpy array operations
4
Indexing, Slicing - Numpy array
5
Quiz
6
Numpy Exercise

Data analysis with Pandas

1
Introduction - Pandas
2
Pandas - Introduction to Series
3
Pandas - Introduction to Dataframe
4
Dataframe - Index, Multiindex
5
Handling Missing Data - dropna, fillna
6
Grouping data
7
Read, Write .csv, .html, excel file
8
Visualization of data with pandas

Data Visulization with Matplotlib

1
Introduction
2
Why Visualization ?
3
MatplotLib - Basic plotting, Plotting terminology
4
MatplotLib - Subplots
5
Matplotlib - Special plot
6
Matplotlib

Data visualization - plotly

1
Plotly - introduction
2
Basic plotting - plotly
3
Exercise : Extend Basic Plot
4
Plotly scatter and line chart
5
Plotly - Bar chart
6
Exercise : Extend Bar Chart
7
Plotly - Bubble chart
8
Plotly - Histogram and Distribution plot

Data visualization with Tableau

1
Introduction to Tableau and Installation
2
Load Data in Tableau
3
Insight -1
4
Insight - 2
5
Save Tableau Worksheet

Introduction to Data

1
Introduction to Data, Continuous and Discrete Data
2
Nominal and Ordinal Data
3
Identify Types of Data

Importing Data in python

1
Introduction
2
Reading Plain text file
3
Reading .csv file
4
Reading Excel and .m Matlab file
5
Read Sqlite Database
6
Fetch Data from Remote file
7
Fetch Data from Facebook API

Data Preprocessing

1
Introduction
2
Reading Data
3
Handling Missing Data
4
Categorical Data
5
Splitting Data in Training and Testing Set
6
Normalize Data

Web Scraping

1
Introduction - Web Scraping
2
What is Web Scraping
3
Web Scraping Process
4
Search Element by TagName and TagByClass
5
How to use developer tools in browser.
6
Web scraping
7
Practical Activity

Exploratory Data analysis

1
EDA of pima indian diabetes dataset
2
Visualize pima indian diabetes dataset

Data transformation and Scaling Data

1
Introduction
2
Rescale data - Standardize data
3
Normalize Data - Binarize Data
4
Practical Activity

Moving towards Machine Learning

1
What is Machine Learning - In Layman term
2
Traditional system of computing vs Machine Learning
3
Formal Definition of Machine Learning
4
How Machine Learning system works
5
Different Types of Machine Learning system- Supervised vs Unsupervised learning
6
Parametric vs Non-parametric machine learning system
7
Machine Learning system design and Scikit learn

Acquire data

pre process data

feature selection

split data

apply ML algorithm

validation

Testing

 

8
Machine Learning application
9
Ask yourself to learn any machine learning algorithm
10
Machine learning introduction

Feature selection for Machine Learning

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Includes

16 hours on-demand video
18 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion