Python programming for Machine Learning , Data Analytics

Python programming for Machine Learning , Data Analytics, available at $49.99, has an average rating of 3.85, with 42 lectures, based on 70 reviews, and has 513 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners of Python programming who are curious about Data Science & Machine Learning It is particularly useful for Beginners of Python programming who are curious about Data Science & Machine Learning.

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Summary

Title: Python programming for Machine Learning , Data Analytics

Price: $49.99

Average Rating: 3.85

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $19.99

Quality Status: approved

Status: Live

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

Who Should Attend

Beginners of Python programming who are curious about Data Science & Machine Learning

Target Audiences

Beginners of Python programming who are curious about Data Science & Machine Learning

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

Course Curriculum

Chapter 1: Setting up the Environment for Python Machine Learning

Lecture 1: Python For machine Learning : Setting up the Environment : Anaconda

Lecture 2: Downloading and Setting up Python and PyCharm IDE

Chapter 2: Python Basics For Machine Learning

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: Software Design – Problem Solving

Lecture 8: Software Design – Flowcharts – Sequence

Lecture 9: Software Design – Repetition

Lecture 10: Flowcharts Questions and Answers # Problem Solving

Chapter 3: 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 4: Data Visualization with Python

Lecture 1: Data preparation and Bar Chart

Lecture 2: Data Visualization with Python Histogram , Pie Chart, etc..

Chapter 5: 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 6: Naive Bayes Classifier with Python [Lecture & Demo]

Lecture 1: Lecture & Demo: Naive bayes classifier

Chapter 7: 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 8: Logistic regression

Lecture 1: Logistic Regression

Chapter 9: 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 [Source code – Complete Project]

Lecture 5: K-Means clustering – Code walkthrough with Theory & Practical

Chapter 10: Extra Reading

Lecture 1: Neural Network Optimization

Lecture 2: Popular resources from Top Universities of the world

Lecture 3: Machine Learning – Source codes

Instructors

Academy of Computing & Artificial Intelligence
Senior Lecturer / Project Supervisor / Consultant

Rating Distribution

1 stars: 6 votes

2 stars: 3 votes

3 stars: 5 votes

4 stars: 13 votes

5 stars: 43 votes

Frequently Asked Questions

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