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: Google's Big Data Approach
Lecture 6: What is a cluster
Lecture 7: What is a Node
Lecture 8: Google File System
Lecture 9: Google's 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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