Data Engineering with Python
Data Engineering with Python, available at $59.99, has an average rating of 3.65, with 63 lectures, based on 59 reviews, and has 444 subscribers.
You will learn about Python Programming Basics For Data Science Supervised Learning – (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest) Unsupervised Learning – Clustering, K-Means clustering KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step This course is ideal for individuals who are Anyone who wish to start the career in Data Science It is particularly useful for Anyone who wish to start the career in Data Science.
Enroll now: Data Engineering with Python
Summary
Title: Data Engineering with Python
Price: $59.99
Average Rating: 3.65
Number of Lectures: 63
Number of Published Lectures: 63
Number of Curriculum Items: 63
Number of Published Curriculum Objects: 63
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- Python Programming Basics For Data Science
- Supervised Learning – (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
- Unsupervised Learning – Clustering, K-Means clustering
- KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step
Who Should Attend
- Anyone who wish to start the career in Data Science
Target Audiences
- Anyone who wish to start the career in Data Science
Academy of Computing & Artificial Intelligence proudly present you the course “Data Engineering with Python”.It all started when the expert team of Academy of Computing & Artificial Intelligence(PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2020.
To make the course more interactive, we have also provided a code demonstration where we explain to you how we could apply each concept/principle [Step by step guidance].
Requirements
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Here’s the checklist:
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A computer – Setup and installation instructions are included.
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Your enthusiasm to learn
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Everything else needed is already included in the course.
At the end of the Course you will understand the basics of Python Programming and the basics of Data Science & Machine learning.
The course will have step by step guidance for machine learning & Data Science with Python.
You can enhance your core programming skills to reach the advanced level.
Setting up the Environment for Python Machine Learning
Understanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)
Data Pre-processing – Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection
Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..
Artificial Neural Networks with Python, KERAS
KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step
Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
Naive Bayes Classifier with Python [Lecture & Demo]
Linear regression
Logistic regression
Introduction to clustering [K – Means Clustering ]
K – Means Clustering
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Python Programming
Setting up the environment
Python For Absolute Beginners : Setting up the Environment : Anaconda
Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary
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Boolean operations
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Conditions , Loops
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(Sequence , Selection, Repetition/Iteration)
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Functions
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File Handling in Python
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Flow Charts
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Algorithms
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Modular Design
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Introduction to Software Design – Problem Solving
Software Design – Flowcharts – Sequence
Software Design – Modular Design
Software Design – Repetition
Flowcharts Questions and Answers # Problem Solving
Does the course get updated?
We continually update the course as well.
What if you have questions?
we offer full support, answering any questions you have.
There’s no risk !
This course comes with a full 30 day money-back guarantee.
Who this course is for:
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Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.
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Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
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Who want to improve their career options by learning the Python Data Engineering skills.
Course Curriculum
Chapter 1: Setting up Python
Lecture 1: Downloading and Setting up Python and PyCharm IDE
Lecture 2: Python For Absolute Beginners : Setting up the Environment : Anaconda
Chapter 2: Python Theory
Lecture 1: Python For Absolute Beginners – Variables – Part 1
Lecture 2: Python For Absolute Beginners – Variables – Part 2
Lecture 3: Python For Absolute Beginners – Variables – Part 3
Lecture 4: Python For Absolute Beginners – Lists
Lecture 5: Python For Absolute Beginners – Lists Part 2
Lecture 6: Python For Absolute Beginners – Lists Part 3
Lecture 7: Python – Conditions – if, if-else and elif Part 1
Lecture 8: Python – Conditions – if, if-else and elif Part 2
Lecture 9: Python – Relational Operators Boolean operators –
Lecture 10: Python For beginners – Loops #Iteration
Lecture 11: Python Programming Tutorial : Loops part 1 #Guess the number program
Lecture 12: Python Programming Tutorial : Loops part 2 #Getting a random number
Lecture 13: Python Programming Tutorial : Loops part 1 #Guess the number program #Modified
Lecture 14: Python program to Find the Class Average
Lecture 15: Python : Functions : Demonstration
Lecture 16: Pass by reference vs value
Lecture 17: Python Function – Arguements (Required, Keyword, Default)
Lecture 18: Python: For Loops #Iteration # Repetition
Lecture 19: Python File Handling – Part 1
Chapter 3: Software Design
Lecture 1: Software Design – Problem Solving
Lecture 2: Software Design – Flowcharts – Sequence
Lecture 3: Software Design – Repetition
Lecture 4: Flowcharts Questions and Answers # Problem Solving
Chapter 4: Python Tutorials
Lecture 1: Tutorial 1 – Introduction
Lecture 2: Tutorial 2 – Built-In Functions
Lecture 3: Tutorial 3 – if conditions
Lecture 4: Tutorial 4 – while loops
Lecture 5: Tutorial 5 – for loops and exceptions
Lecture 6: Tutorial 6 – for loop challenge questions
Lecture 7: Tutorial 7 – Guess the Word Game
Lecture 8: Tutorial 8 – Functions (Dragon Kingdom Game)
Chapter 5: Setting up the Environment for Machine Learning
Lecture 1: Downloading and Setting up Anaconda for Machine Learning
Chapter 6: Understanding Data With Statistics & Data Pre-processing
Lecture 1: Understanding Data with Statistics: Reading data from file
Lecture 2: Understanding Data with Statistics: Checking dimensions of Data
Lecture 3: Understanding Data with Statistics: Statistical Summary of Data
Lecture 4: Understanding Data with Statistics: Correlation between attributes
Lecture 5: Data Pre-processing – Scaling with a demonstration in python
Lecture 6: Data Pre-processing – Normalization , Binarization , Standardization in Python
Lecture 7: Feature Selection Techniques : Univariate Selection
Chapter 7: Data Visualization with Python
Lecture 1: Data preparation and Bar Chart
Lecture 2: Data Visualization with Python Histogram , Pie Chart, etc..
Chapter 8: Artificial Neural Networks [Comprehensive Sessions]
Lecture 1: Introduction to Artificial Neural Networks
Lecture 2: Creating the First ANN from Scratch with Python
Lecture 3: Multiple Input Neuron
Lecture 4: Creating a simple layer of neurons, with 4 inputs. # Python # From scratch
Lecture 5: ANN – Illustrative Example
Lecture 6: KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step
Lecture 7: Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project]
Chapter 9: Naive Bayes Classifier with Python [Lecture & Demo]
Lecture 1: Lecture & Demo: Naive bayes classifier
Chapter 10: Linear regression
Lecture 1: Linear regression
Lecture 2: Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression
Lecture 3: Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression
Lecture 4: Multivariate Linear Regression Demo [Hands-on] Linear Regression
Chapter 11: Logistic regression
Lecture 1: Logistic Regression
Chapter 12: Introduction to clustering [K – Means Clustering ]
Lecture 1: What is clustering in Machine Learning
Lecture 2: K – Means Clustering
Lecture 3: [hands-on] K – Means clustering with python step by step implementation
Lecture 4: K-Means clustering – Code walkthrough with Theory & Practical
Chapter 13: Extra Reading
Lecture 1: Neural Network Optimization
Lecture 2: Popular resources from Top Universities of the world
Lecture 3: Machine Learning – Source codes
Instructors
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Academy of Computing & Artificial Intelligence
Senior Lecturer / Project Supervisor / Consultant
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 6 votes
- 3 stars: 9 votes
- 4 stars: 15 votes
- 5 stars: 23 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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