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Complete Data Science & Machine Learning Bootcamp in Python

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Complete Data Science & Machine Learning Bootcamp in Python, available at $59.99, has an average rating of 4.15, with 365 lectures, based on 77 reviews, and has 875 subscribers.

You will learn about Python for data science The data science process NumPy for numerical computation Pandas for data manipulation Matplotlib for visualization Seaborn for beautiful visuals Plotly for interactive visuals Introduction to machine learning Dask for big data LightGBM XGBoost CatBoost Linear regression Logistic regression Decision trees Random forest Deep learning using Keras and TensorFlow Artificial Neural Networks Convolutional Neural Networks Natural language processing Support Vector Machines KNearest Neighbors Statistical Testing K-Means clustering Principal Component Analysis Association Rule Mining – Apriori Building Dashboards in Power BI Data Science Applications with Dash Apache Spark in Python Google Data Studio This course is ideal for individuals who are People who would like to get started with data science and machine learning without any prior knowledge or Persons who would like to refresh their data science and machine learning knowledge It is particularly useful for People who would like to get started with data science and machine learning without any prior knowledge or Persons who would like to refresh their data science and machine learning knowledge.

Enroll now: Complete Data Science & Machine Learning Bootcamp in Python

Summary

Title: Complete Data Science & Machine Learning Bootcamp in Python

Price: $59.99

Average Rating: 4.15

Number of Lectures: 365

Number of Published Lectures: 365

Number of Curriculum Items: 365

Number of Published Curriculum Objects: 365

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Python for data science
  • The data science process
  • NumPy for numerical computation
  • Pandas for data manipulation
  • Matplotlib for visualization
  • Seaborn for beautiful visuals
  • Plotly for interactive visuals
  • Introduction to machine learning
  • Dask for big data
  • LightGBM
  • XGBoost
  • CatBoost
  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forest
  • Deep learning using Keras and TensorFlow
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Natural language processing
  • Support Vector Machines
  • KNearest Neighbors
  • Statistical Testing
  • K-Means clustering
  • Principal Component Analysis
  • Association Rule Mining – Apriori
  • Building Dashboards in Power BI
  • Data Science Applications with Dash
  • Apache Spark in Python
  • Google Data Studio

Who Should Attend

  • People who would like to get started with data science and machine learning without any prior knowledge
  • Persons who would like to refresh their data science and machine learning knowledge

Target Audiences

  • People who would like to get started with data science and machine learning without any prior knowledge
  • Persons who would like to refresh their data science and machine learning knowledge

Obtain skills in one of the most sort after fields of this century

In this course, you’ll learn how to get started in data science. You don’t need any prior knowledge in programming. We’ll teach you the Python basics you need to get started.  Here are some of the items we will cover in this course

  • The Data Science Process

  • Python for Data Science

  • NumPy for Numerical Computation

  • Pandas for Data Manipulation

  • Matplotlib for Visualization

  • Seaborn for Beautiful Visuals

  • Plotly for Interactive Visuals

  • Introduction to Machine Learning

  • Dask for Big Data

  • Power BI Desktop

  • Google Data Studio

  • Association Rule Mining – Apriori

  • Deep Learning

  • Apache Spark for Handling Big Data

For the machine learning section here are some items we’ll cover :

  • How Algorithms Work

  • Advantages & Disadvantages of Various Algorithms

  • Feature Importances

  • Metrics

  • Cross-Validation

  • Fighting Overfitting

  • Hyperparameter Tuning

  • Handling Imbalanced Data

  • TensorFlow & Keras

  • Automated Machine Learning(AutoML)

  • Natural Language Processing

The course also contains exercises and solutions that will help you practice what you have learned.

By enrolling in this course, you’ll have lifetime access to the videos and Notebooks.Purchasing the course also comes with a 30-day money-back guarantee,so you can try it at no risk at all.

Let’s now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course.

The course also contains exercises and solutions that will help you practice what you have learned.

By enrolling in this course, you’ll have lifetime access to the videos and Notebooks.Purchasing the course also comes with a 30-day money-back guarantee,so you can try it at no risk at all.

Let’s now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course.

The course also contains exercises and solutions that will help you practice what you have learned.

By enrolling in this course, you’ll have lifetime access to the videos and Notebooks.Purchasing the course also comes with a 30-day money-back guarantee,so you can try it at no risk at all.

Let’s now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Plan of Attack

Lecture 2: Assignment: Introduce yourself

Lecture 3: Install Anaconda

Lecture 4: Understand the Data Science Process

Lecture 5: About Udemy Reviews

Lecture 6: Housekeeping (Don't Skip)

Chapter 2: Understand Python for Data Science

Lecture 1: Python for Data Science

Lecture 2: Linux Launch Notebook

Lecture 3: Windows Launch Notebook

Lecture 4: Download All Notebooks

Lecture 5: Folder Structure

Lecture 6: Python Operations & Comments

Lecture 7: Python Data Types

Lecture 8: Python Lists

Lecture 9: Lists – Negative Indexing

Lecture 10: Python Dictionaries

Lecture 11: Python Tuples

Lecture 12: Python Sets

Lecture 13: Python Boolean Type

Lecture 14: Conditional Statements

Lecture 15: Python Functions

Lecture 16: Python For Loop

Lecture 17: Python While Loop

Lecture 18: Python Map Function

Lecture 19: Python Range Function

Lecture 20: Python Exercise

Lecture 21: Python Project Solutions

Chapter 3: Package Management

Lecture 1: Section Introduction

Lecture 2: pip & virtualenv Intuition

Lecture 3: pip & virtualenv Practical

Lecture 4: Installing Packages using the Anaconda Navigator

Chapter 4: NumPy for Numerical Computation

Lecture 1: Section Introduction

Lecture 2: NumPy Introduction

Lecture 3: NumPy Arrays

Lecture 4: Checking Documentation in Notebooks

Lecture 5: Indexing One Dimensional Array

Lecture 6: Indexing Multi-dimensional Array

Lecture 7: Broadcasting in NumPy

Lecture 8: NumPy Operations

Lecture 9: NumPy Project

Lecture 10: NumPy Project Solutions

Chapter 5: Manipulate Data using Pandas

Lecture 1: Section Introduction

Lecture 2: Pandas Introduction

Lecture 3: Pandas DataFrame

Lecture 4: Resetting the Index

Lecture 5: Deleting Columns

Lecture 6: Dealing with Null Values

Lecture 7: Creating New Columns

Lecture 8: Selecting Data in Pandas

Lecture 9: Grouping Data in Pandas

Lecture 10: Exporting a Pandas DataFrame

Lecture 11: Loading Datasets

Lecture 12: Creating Pivot Tables

Lecture 13: Pandas Project

Chapter 6: Descriptive Statistics

Lecture 1: Descriptive Statistics

Chapter 7: Pandas Project Solutions

Lecture 1: Part 1

Lecture 2: Part 2

Lecture 3: Part 3

Lecture 4: Part 4

Lecture 5: Part 5

Lecture 6: Part 6

Lecture 7: Part 7

Chapter 8: Data Visualization Guide

Lecture 1: Visualization Guide

Chapter 9: Data Visualization in Matplotlib

Lecture 1: Section Introduction

Lecture 2: Matplotlib Vertical Bar Plot

Lecture 3: Matplotlib Horizontal Bar Plot

Lecture 4: Matplotlib Scatter Plot

Lecture 5: Matplotlib Histogram

Lecture 6: Matplotlib Pie Chart

Lecture 7: Matplotlib Line Plot

Lecture 8: Matplotlib Subplots

Lecture 9: Matplotlib Figure & Axes Part one

Lecture 10: Matplotlib Figure & Axes Part Two

Lecture 11: Matplotlib Project & Solutions

Chapter 10: Data Visualization in Seaborn – Categorical Plots

Lecture 1: Seaborn Count Plot

Lecture 2: Seaborn Violin Plot

Lecture 3: Seaborn – Adding Hue

Lecture 4: Seaborn Strip Plot

Lecture 5: Swarm Plot with Hue

Lecture 6: Seaborn Order X Values

Lecture 7: Strip Plot with Hue

Lecture 8: Seaborn Boxplot

Lecture 9: Seaborn Boxen Plot

Lecture 10: Seaborn Barplot

Chapter 11: Data Visualization in Seaborn – Visualizing Distributions

Lecture 1: Joint & Scatter Plots

Lecture 2: Seaborn Hexagonal Bins & Kernel Density Estimation

Lecture 3: Seaborn Distplot

Lecture 4: Seaborn Pair Plot

Lecture 5: Seaborn Line Plot

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 0 votes
  • 3 stars: 13 votes
  • 4 stars: 28 votes
  • 5 stars: 34 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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