Machine Learning with Python and Statistics

Machine Learning with Python and Statistics, available at $59.99, has an average rating of 3.75, with 176 lectures, 11 quizzes, based on 12 reviews, and has 115 subscribers.

You will learn about Complete course on Python from beginner to Advance Level This course is ideal for individuals who are This course is targeted towards anyone who aims to master Python as a programming language from absolute scratch with experience on replica of real time assignments It is particularly useful for This course is targeted towards anyone who aims to master Python as a programming language from absolute scratch with experience on replica of real time assignments.

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Summary

Title: Machine Learning with Python and Statistics

Price: $59.99

Average Rating: 3.75

Number of Lectures: 176

Number of Quizzes: 11

Number of Published Lectures: 155

Number of Published Quizzes: 11

Number of Curriculum Items: 187

Number of Published Curriculum Objects: 166

Original Price: ?1,499

Quality Status: approved

Status: Live

What You Will Learn

Complete course on Python from beginner to Advance Level

Who Should Attend

This course is targeted towards anyone who aims to master Python as a programming language from absolute scratch with experience on replica of real time assignments

Target Audiences

This course is targeted towards anyone who aims to master Python as a programming language from absolute scratch with experience on replica of real time assignments

This course is specifically designed for students to learn the concepts in Python, Statistics and Maching Learning. We have tailored this curriculum so that even non-technical students can opt this course and understand the complex concepts. This course includes concepts in Python such as: Variables, functions,Pandas, Numpy, exception handling, web scraping, multithreading,connecting to database, matplotlib, modules, packages,files, flask,grammer correction and speech to text conversion. Projects in Python such as Hangman, Snake Game, Phonebook and Password Generator.

For Statistics it includes concepts such as Inferential statistics, Descriptive statistics,data types, population, Central Tendencies, Measures of Dispersion,Z-score, Min-max scaling, Co-variance, Correlation, Multi-collinearity, Anova, Kurtosis,Normal Distribution, Poisson Distribution,Bionominal Distribution,Hypothesis Testing, Central Limit Theorem, Degrees Of Freedom, Confidence Interval, P-value.

It also covers important Machine Learning algorithms such as Linear Regression, Logistic Regression,Confusion Matrix, Cost Matrix, Naive Bayes, K-Nearest Neighbors, Decision Tree Algorithm, Random Forest Algorithm,Support Vector Machine, Polynomial Regression, Unsupervised Learning, K-Means Clustering, Principal Component Analysis, DBSCAN, Linear Discriminant Analysis, Linear regression, Logistic Regression, Naive Bayes, KNN, Decision Tree, Support Vector Machine, K means Clustering, Principal Component Analysis, Hierarchical Clustering and Docker for Machine Learning. We have also included ‘Deployment of Machine Learning’ as one of the section so that user can learn to built the model from scratch and deploy it on its own.

Course Curriculum

Chapter 1: Introduction to python

Lecture 1: Importance of Python Part 1

Lecture 2: Importance of Python Part 2

Lecture 3: Hands-on Exercise- Installing Python Anaconda for the Windows, Linux and Mac

Chapter 2: Variables

Lecture 1: Variables Introduction

Lecture 2: Categorical Variable

Lecture 3: Numerical Variable

Lecture 4: Mixed Variable

Chapter 3: Python Lists

Lecture 1: Adding Attribute

Lecture 2: Sets And Tupels

Lecture 3: Conditional Statements

Lecture 4: Looping Statements

Chapter 4: Functions in Python

Lecture 1: Python Functions- Introduction

Lecture 2: Built-in Functions-3

Lecture 3: User defined functions

Lecture 4: Date and Time Function

Chapter 5: Pandas and Numpy

Lecture 1: Pandas and Numpy

Lecture 2: Numpy-libraries

Lecture 3: Numpy-Indexing and selection

Chapter 6: Classes, Objects and Modules

Lecture 1: Classes and Objects

Lecture 2: Object Oriented Programming And Classes

Lecture 3: Abstraction in OOPs

Lecture 4: Abstract Class and Methods

Lecture 5: Inheritance in OOPs

Lecture 6: Encapsulation

Chapter 7: Exception Handling

Lecture 1: Introduction To Exception Handling

Lecture 2: Exception Handling Part 2

Chapter 8: Web Scraping with Python

Lecture 1: Introduction To Web Scraping In Python

Lecture 2: Introduction With Basic Html

Lecture 3: Web Crawler, Web Scraping

Chapter 9: Multi Threading

Lecture 1: Introduction To Threading

Lecture 2: How To Use Multithreading- Code

Chapter 10: Connecting DataBase to Python

Lecture 1: Connect Python With MySQL

Chapter 11: Matplotlib

Lecture 1: How To Plot Graph And Chart With Python

Lecture 2: Histogram

Lecture 3: Barchart

Lecture 4: Pie-chart

Lecture 5: Plot Using Python

Lecture 6: Scatterplot

Lecture 7: All Plots

Chapter 12: Tools in Python

Lecture 1: Code Quality

Chapter 13: Modules

Lecture 1: Imports

Chapter 14: Packages

Lecture 1: Decorators

Lecture 2: Generators

Chapter 15: Files

Lecture 1: File Processing Part 1

Lecture 2: File Processing Part 2

Chapter 16: Flask section

Lecture 1: Flask Introduction

Lecture 2: Creating Our First App

Lecture 3: Querying Database

Lecture 4: Dynamic Url Query

Chapter 17: Testing and Publishing

Lecture 1: Testing, Comments And Publish

Chapter 18: User Interface

Lecture 1: How To Create And Design User Interface

Lecture 2: How To Create A Button?

Chapter 19: Application Development

Lecture 1: Tic-tac-toe Game

Chapter 20: Grammer Correction in Text

Lecture 1: Understanding The Concept Of Text Grammar

Lecture 2: Grammatical Rules

Lecture 3: Appling Nlp Rule For Grammar Correction – Part 1

Lecture 4: Appling Nlp Rule For Grammar Correction – Part 2

Lecture 5: Appling Nlp Rule For Grammar Correction – Part 3

Lecture 6: Introduction To Speech To Text Conversion

Lecture 7: Implementation Example Of Speech To Text

Chapter 21: Mini and Mega Python Projects

Lecture 1: The Hangman Game Description

Lecture 2: The Hangman Game Code

Lecture 3: Snake Game Description

Lecture 4: Snake Game Code

Lecture 5: Phonebook (Add, Delete, Updated Contacts) Description

Lecture 6: Phonebook (Add, Delete, Updated Contacts) Code

Lecture 7: Password Generator Description

Chapter 22: Overview of Statistics

Lecture 1: Overview of Statistics

Lecture 2: What Is Descriptive Statistics?

Chapter 23: Introduction to basic definition used in Statistics

Lecture 1: Data Types

Lecture 2: Population

Lecture 3: Sample

Chapter 24: Concepts to Understand Data

Lecture 1: Central Tendencies

Lecture 2: Measures Of Dispersion

Chapter 25: Methods of Feature Scaling in Statistics

Lecture 1: Z-score

Instructors

Saurabh Mirgane
Product Manager

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 4 votes

5 stars: 5 votes

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