Python programming for Machine Learning , Data Analytics

Learn to create Machine Learning Algorithms in Python # Introduction to Data Science and Machine Learning [Step by Step]

4.25 (69 reviews)
Udemy
platform
English
language
Data Science
category
Python programming for Machine Learning , Data Analytics
512
students
7.5 hours
content
Jun 2020
last update
$49.99
regular price

What you will learn

Data Science & Machine Learning with Python

Data analytics

Understanding Data With Statistics & Data Pre-processing

Data Visualization with Python

Artificial Neural Networks with Python

Linear regression

Logistic regression

Introduction to clustering [K - Means Clustering ]

Deep Learning -Handwritten Digits Recognition

Python Programming

Description

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. You will learn about Software Design as well. eg: Flow charts, pseudacodes, algorithms. By the end of these videos, you will get the understanding of following areas the

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

Content

Setting up the Environment for Python Machine Learning

Python For machine Learning : Setting up the Environment : Anaconda
Downloading and Setting up Python and PyCharm IDE

Python Basics For Machine Learning

Python For Absolute Beginners - Variables - Part 1
Python For Absolute Beginners - Variables - Part 2
Python For Absolute Beginners - Variables - Part 3
Python For Absolute Beginners - Lists
Python For Absolute Beginners - Lists Part 2
Python For Absolute Beginners - Lists Part 3
Software Design - Problem Solving
Software Design - Flowcharts - Sequence
Software Design - Repetition
Flowcharts Questions and Answers # Problem Solving

Understanding Data With Statistics & Data Pre-processing

Understanding Data with Statistics: Reading data from file
Understanding Data with Statistics: Checking dimensions of Data
Understanding Data with Statistics: Statistical Summary of Data
Understanding Data with Statistics: Correlation between attributes
Data Pre-processing - Scaling with a demonstration in python
Data Pre-processing - Normalization , Binarization , Standardization in Python
feature Selection Techniques : Univariate Selection

Data Visualization with Python

Data preparation and Bar Chart
Data Visualization with Python Histogram , Pie Chart, etc..

Artificial Neural Networks [ Comprehensive Sessions]

Introduction to Artificial Neural Networks
Creating the First ANN from Scratch with Python
Multiple Input Neuron
Creating a simple layer of neurons, with 4 inputs. # Python # From scratch
ANN - Illustrative Example
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]

Lecture & Demo: Naive bayes classifier

Linear regression

Linear regression
Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression
Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression
Multivariate Linear Regression Demo [Hands-on] Linear Regression

Logistic regression

Logistic Regression

Introduction to clustering [K - Means Clustering ]

What is clustering in Machine Learning
K - Means Clustering
[hands-on] K - Means clustering with python step by step implementation
K - Means Clustering [Source code - Complete Project]
K-Means clustering - Code walkthrough with Theory & Practical

Extra Reading

Neural Network Optimization
Popular resources from Top Universities of the world

Reviews

Sangeeth
August 4, 2020
Great course for ML and Data Analytics, provides in-depth knowledge on core concepts. I recommend to others!!!
Musa
July 10, 2020
No proper introduction to the basics. For instance I should be introduced to machine learning data science, and so on. Zero level introduction to any of the courses, if I have chance I will change the course.
Frank
June 25, 2020
Great content great course!. Achala is a lecturer by profession and has been teaching many students. I thought of enrolling in this course to upgrade my skills. His course is wonderful.
Thilini
May 17, 2020
Awesome course content. Easy to follow. Recommended for beginners to professionals who wants to get theroy and hands-on experience with python programming.
Ayeshmantha
April 21, 2020
Great Course! Can learn the basics of python used for machine learning and a few algorithms used in machine learning. Furthermore, live demonstrations of code have been provided to enhance the learning experience.

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2828518
udemy ID
2/21/2020
course created date
5/22/2020
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