Data Science and Machine Learning in Python

Data Science and Machine Learning in Python, available at $54.99, has an average rating of 4.45, with 126 lectures, based on 49 reviews, and has 269 subscribers.

You will learn about Machine Learning in Python Complete SQL BootCamp Using PostgreSQL TABLEAU – The Best Visualization Software Data Science concepts Data Wrangling, Cleaning and Data Preparation for Machine Learning Supervised and Unsupervised machine learning Python Model Selection Feature Engineering Dimensionality Reduction Regression Classification This course is ideal for individuals who are Beginner Data Science/Machine Learning Enthusiast who want to step into the world of Machine Learning. or Anyone who wants to be become a Data Scientist It is particularly useful for Beginner Data Science/Machine Learning Enthusiast who want to step into the world of Machine Learning. or Anyone who wants to be become a Data Scientist.

Enroll now: Data Science and Machine Learning in Python

Summary

Title: Data Science and Machine Learning in Python

Price: $54.99

Average Rating: 4.45

Number of Lectures: 126

Number of Published Lectures: 120

Number of Curriculum Items: 126

Number of Published Curriculum Objects: 120

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning in Python
Complete SQL BootCamp Using PostgreSQL
TABLEAU – The Best Visualization Software
Data Science concepts
Data Wrangling, Cleaning and Data Preparation for Machine Learning
Supervised and Unsupervised machine learning
Python
Model Selection
Feature Engineering
Dimensionality Reduction
Regression
Classification

Who Should Attend

Beginner Data Science/Machine Learning Enthusiast who want to step into the world of Machine Learning.
Anyone who wants to be become a Data Scientist

Target Audiences

Beginner Data Science/Machine Learning Enthusiast who want to step into the world of Machine Learning.
Anyone who wants to be become a Data Scientist

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Harvard suggest that one of most important jobs in 21st century is a “Data Scientist”

Data Scientist earn an average salary of a data scientist is over $120,000 in the USA ! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

If you have some programming experience or you are an experienced developers who is looking to turbo charge your career in Data Science. This course is for you!

You don’t need to spend thousand of dollars on other course , this course provides all the same information at a very low cost..

With over 125+ HD lectures(Python, Machine Learning, SQL,TABLEAU) and detailed code notebooks for every lecture, it is an extremely detailed course available on Udemy.

Basically everything you need to BECOME A DATA SCIENTIST IN ONE PLACE!!

You will learn true machine learning with Python, programming in python, data wrangling in Python and creating visualizations.

Some of the topics you will be learning:

Programming with Python
NumPy with Python
Data Wrangling in Python
Use pandas to handle Excel Files, text file, JSON, Cloud(AWS) and others
Connecting Python to SQL
Use Seaborn for data visualizations
Complete SQL Using PostgreSQL
TABLEAU – One of the best data visualization software
Machine Learning with SciKit Learn, including:
Linear Regression
Logistic Regression
K Nearest Neighbors
K Means Clustering
Decision Trees
Random Forests
Support Vector Machines
Naive Bayes
Hyper Parameter tuning
Feature Engineering
Model Selection
and much, much more!

Enroll in the course and become a data scientist today!

Course Curriculum

Chapter 1: Welcome to the Course.

Lecture 1: Welcome to the Course!

Lecture 2: Installing Python and Anaconda – Windows,Mac or Linux

Lecture 3: ***Update on Udemy Reviews***

Lecture 4: Recommended Anaconda Version

Lecture 5: Basics of Jupyter Notebook

Lecture 6: Course Notes

Chapter 2: Python Crash Course

Lecture 1: Python Crash Course Part 1

Lecture 2: Python Crash Course Part 2

Lecture 3: Python Crash Course Part 3

Lecture 4: Python Exercises

Lecture 5: Python Exercises Solutions

Chapter 3: Numpy Basics

Lecture 1: Numpy Operations Part 1

Lecture 2: Numpy Operations Part 2

Lecture 3: Numpy Operations Part 3

Lecture 4: Numpy Exercises Overview

Lecture 5: Numpy Exercises Solution Overview

Chapter 4: Data Wrangling in Python: Pandas

Lecture 1: Introduction to Pandas.

Lecture 2: Pandas : Basics Functions

Lecture 3: Pandas : Slicing and Row Selection

Lecture 4: Pandas : Descriptive Statistics

Lecture 5: Pandas: Missing and Cleaning Data

Lecture 6: Pandas: Groupby and Indexing

Lecture 7: Pandas: Pivot Table & CrossTab

Lecture 8: Pandas:TimeSeries data operation.

Lecture 9: Pandas: Merging, Joining and Concatenating Dataframes

Lecture 10: Pandas: Importing and Exporting Data – CSV/Excel/AWS/SQL/Online

Lecture 11: In-Built Visualization in Pandas

Lecture 12: Pandas Exercise

Lecture 13: Pandas Exercise Solution

Chapter 5: Plotting Data in Python : Seaborn

Lecture 1: Seaborn Introduction

Lecture 2: Case Study 1 – Visualizing Data Distribution Using Seaborn

Lecture 3: Case Study 2 – Plotting Categorical Variables Using Seaborn

Lecture 4: Case Study 3 – Plotting Linear Relationships

Lecture 5: Case Study 4 – Visualizing Statistical Relationship

Chapter 6: Introduction to Machine Learning

Lecture 1: Machine Learning Algorithmns Overview

Lecture 2: Scikit-Learn Introduction

Lecture 3: Data Processing – Standardization & Normalization,OneHotEncoding

Lecture 4: Data Processing – Train_Test_Split

Lecture 5: Machine Learning PreProcessing Template

Chapter 7: Supervised Learning – Regression

Lecture 1: Linear Regression Intuition

Lecture 2: Linear Regression Overview

Lecture 3: Linear Regression Exercise Overview

Lecture 4: Linear Regression Solutions Overview

Lecture 5: KNeighborsRegressor -Intuition

Lecture 6: Decision Tree Regressor

Lecture 7: RandomForestRegressor – Intuition

Lecture 8: Support Vector Regression Intuition

Lecture 9: RANSAC Regressor – Intuition

Lecture 10: Lasso Regressor – Intuition

Lecture 11: Ridge Regression – Intuition

Lecture 12: Gaggle of Regressors – Overview

Lecture 13: Gaggle of Regressors Exercise Overview

Lecture 14: Gaggle of Regressors Solution Overview

Chapter 8: Supervised Learning – Classification

Lecture 1: Classification Models Intuition

Lecture 2: Logistic Regression Intuition

Lecture 3: Logistic Regression Overview Part 1

Lecture 4: Logistic Regression Overview Part 2

Lecture 5: Logistic Regression Exercise Overview

Lecture 6: Logistic Regression Exercise Solution Overview

Lecture 7: KNeighbours Classifier

Lecture 8: KNeighbours Classifier

Lecture 9: Decision Tree & Random Forest Classifier

Lecture 10: Decision Tree and Random Forest Classifier & Model Selection

Lecture 11: Support Vector Machines Classifier

Lecture 12: Naives Bayes Classifier

Lecture 13: Gaggle of Classifiers

Lecture 14: Gaggle of Classifiers – Exercise Overview

Lecture 15: Gaggle of Classifiers – Exercise Solutions Overview

Chapter 9: UnSupervised Learning – Clustering

Lecture 1: KMeans Clustering

Chapter 10: Model Selection and Dimensionality Reduction

Lecture 1: Model Selection and HyperParameters

Lecture 2: Feature Engineering & Dimensionality Reduction

Chapter 11: BONUS : Extensive SQL BootCamp

Lecture 1: SQL With PostgreSQL Introduction

Lecture 2: Setting up PostgreSQL

Lecture 3: Course Files

Lecture 4: PgAdmin 4 Overview

Lecture 5: Import Database in PostgreSQL

Lecture 6: SELECT

Lecture 7: ORDER BY

Lecture 8: SELECT DISTINCT

Lecture 9: WHERE CLAUSE

Lecture 10: LIMIT

Lecture 11: FETCH

Lecture 12: LIKE

Lecture 13: IN CLAUSE

Lecture 14: BETWEEN AND ALIAS – AS

Lecture 15: IS NULL CLAUSE

Lecture 16: JOINS INTUITION

Lecture 17: INNER JOIN

Lecture 18: LEFT JOIN

Instructors

Gaurav Chauhan
Data and Cloud Architect

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 20 votes
5 stars: 21 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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