Data Engineering with Python

What you'll 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


Requirements

Computer & Internet Connection

Description

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

Here’s the checklist:

A computer - Setup and installation instructions are included.

Your enthusiasm to learn

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

Python Programming

Setting up the environment

Python For Absolute Beginners : Setting up the Environment : Anaconda

Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary

Boolean operations

Conditions , Loops

(Sequence , Selection, Repetition/Iteration)

Functions

File Handling in Python

Flow Charts

Algorithms

Modular Design

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.

Who this course is for:

Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.
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.

Who want to improve their career options by learning the Python Data Engineering skills.

Anyone who wish to start the career in Data Science