Data Science and Machine Learning For Beginners with Python

Data Science and Machine Learning For Beginners with Python, available at $19.99, has an average rating of 4.55, with 79 lectures, based on 547 reviews, and has 30090 subscribers.

You will learn about Install Jupyter Notebook Server Create a new notebook Explore Components of Jupyter Notebook Understand Data Science Life Cycle Use Kaggle Data Sets Perform Probability Sampling Explore and use Tabular Data Explore Pandas DataFrame Manipulate Pandas DataFrame Perform Data Cleaning Perform Data Visualization Visualize Qualitative Data Explore Machine Learning Frameworks Understand Supervised Machine Learning Use machine learning to predict value of a house Use Scikit-Learn Load datasets Make Predictions using machine learning Understand Python Expressions and Statements Understand Python Data Types and how to cast data types Understand Python Variables and Data Structures Understand Python Conditional Flow and Functions Learn SQL with PostgreSQL Perform SQL CRUD Operations on PostgreSQL Database Filter and Sort Data using SQL Understand Big Data Terminologies. This course is ideal for individuals who are Beginners to Data Science or Beginners to Machine Learning or Beginners to Python or Beginners to SQL It is particularly useful for Beginners to Data Science or Beginners to Machine Learning or Beginners to Python or Beginners to SQL.

Enroll now: Data Science and Machine Learning For Beginners with Python

Summary

Title: Data Science and Machine Learning For Beginners with Python

Price: $19.99

Average Rating: 4.55

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will Learn

Install Jupyter Notebook Server

Create a new notebook

Explore Components of Jupyter Notebook

Understand Data Science Life Cycle

Use Kaggle Data Sets

Perform Probability Sampling

Explore and use Tabular Data

Explore Pandas DataFrame

Manipulate Pandas DataFrame

Perform Data Cleaning

Perform Data Visualization

Visualize Qualitative Data

Explore Machine Learning Frameworks

Understand Supervised Machine Learning

Use machine learning to predict value of a house

Use Scikit-Learn

Load datasets

Make Predictions using machine learning

Understand Python Expressions and Statements

Understand Python Data Types and how to cast data types

Understand Python Variables and Data Structures

Understand Python Conditional Flow and Functions

Learn SQL with PostgreSQL

Perform SQL CRUD Operations on PostgreSQL Database

Filter and Sort Data using SQL

Understand Big Data Terminologies.

Who Should Attend

Beginners to Data Science

Beginners to Machine Learning

Beginners to Python

Beginners to SQL

Target Audiences

Beginners to Data Science

Beginners to Machine Learning

Beginners to Python

Beginners to SQL

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information . Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development.   Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights.

Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it’s flexibility. Python is used a lot in data science. 

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we’ll explore some basic machine learning concepts and load data to make predictions.

We will also be using SQL to interact with data inside a PostgreSQL Database.

What you’ll learn

Understand Data Science Life Cycle

Use Kaggle Data Sets

Perform Probability Sampling

Explore and use Tabular Data

Explore Pandas DataFrame

Manipulate Pandas DataFrame

Perform Data Cleaning

Perform Data Visualization

Visualize Qualitative Data

Explore Machine Learning Frameworks

Understand Supervised Machine Learning

Use machine learning to predict value of a house

Use Scikit-Learn

Load datasets

Make Predictions using machine learning

Understand Python Expressions and Statements

Understand Python Data Types and how to cast data types

Understand Python Variables and Data Structures

Understand Python Conditional Flow and Functions

Learn SQL with PostgreSQL

Perform SQL CRUD Operations on PostgreSQL Database

Filter and Sort Data using SQL

Understand Big Data Terminologies

A Data Scientist can work as the following:

data analyst.

machine learning engineer.

business analyst.

data engineer.

IT system analyst.

data analytics consultant.

digital marketing manager.

Course Curriculum

Chapter 1: Introduction and Setup

Lecture 1: Introduction

Lecture 2: What is Jupyter Notebook

Lecture 3: Installing Jupyter Notebook Server

Lecture 4: Running Jupyter Notebook Server

Lecture 5: Common Jupyter Notebook Commands

Lecture 6: Jupyter Notebook Components

Lecture 7: Jupyter Notebook Dashboard

Lecture 8: Jupyter Notebook User Interface

Lecture 9: Creating a new Notebook

Chapter 2: Python Fundamentals

Lecture 1: What is Python

Lecture 2: Python Expressions

Lecture 3: Python Statements

Lecture 4: Python Comments

Lecture 5: Python Data Types

Lecture 6: Casting Data Type

Lecture 7: Python Variables

Lecture 8: Python List

Lecture 9: Python Tuple

Lecture 10: Python Dictionaries

Lecture 11: Python Operators

Lecture 12: Python Conditional Statements

Lecture 13: Python Loops

Lecture 14: Python Functions

Chapter 3: Data Science

Lecture 1: What is Data Science

Lecture 2: Impact of Data Science

Lecture 3: Data Science life cycle

Lecture 4: Data Science Terminologies

Lecture 5: Kaggle Data Sets

Lecture 6: Probability Sampling

Lecture 7: Tabular Data

Lecture 8: Exploring Pandas DataFrame

Lecture 9: Manipulating a Pandas DataFrame

Lecture 10: What is Data Cleaning

Lecture 11: Basic Data Cleaning Process

Lecture 12: What is Data Visualization

Lecture 13: Visualizing Qualitative Data : Part 1

Lecture 14: Visualizing Qualitative Data : Part 2

Chapter 4: Introduction to Machine Learning with Python

Lecture 1: Installing Python

Lecture 2: Installing Pycharm on Windows

Lecture 3: Installing Pycharm on Macs

Lecture 4: Installing Anaconda

Lecture 5: What is Machine Learning

Lecture 6: Machine Learning Frameworks

Lecture 7: Machine Learning Vocabulary

Lecture 8: Supervised machine learning

Lecture 9: Where Machine Learning is used

Lecture 10: Creating a basic house value estimator

Lecture 11: Using Scikit-Learn

Lecture 12: Loading a dataset part 1

Lecture 13: Loading a dataset part 2

Lecture 14: Making Predictions part 1

Lecture 15: Making Predictions part 2

Chapter 5: SQL and Data Science with PostgreSQL

Lecture 1: What is SQL

Lecture 2: What is PostgreSQL

Lecture 3: Installing PostgreSQL on windows

Lecture 4: Installing PostgreSQL on Mac

Lecture 5: Connecting to a PostgreSQL Database

Lecture 6: Database Concepts

Lecture 7: Install Sample Database

Lecture 8: What is CRUD

Lecture 9: Data Types

Lecture 10: SQL CREATE TABLE Statement

Lecture 11: SQL INSERT Statement

Lecture 12: SQL SELECT Statement

Lecture 13: SQL UPDATE Statement

Lecture 14: SQL WHERE clause

Lecture 15: SQL ORDER BY Clause

Chapter 6: Introduction to Big Data Terminology

Lecture 1: What is Big Data

Lecture 2: What is high volume

Lecture 3: What is high variety

Lecture 4: What is high velocity

Lecture 5: Googles Big Data Approach

Lecture 6: What is a cluster

Lecture 7: What is a Node

Lecture 8: Google File System

Lecture 9: Googles Big Table

Lecture 10: What is MapReduce

Lecture 11: Apache Hadoop

Lecture 12: Thank You

Instructors

Bluelime Learning Solutions
Making Learning Simple

Rating Distribution

1 stars: 11 votes

2 stars: 25 votes

3 stars: 111 votes

4 stars: 192 votes

5 stars: 208 votes

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