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30-Day Money-Back Guarantee

This course includes:

  • 17.5 hours on-demand video
  • 21 articles
  • 43 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Data Analysis

Complete Data Science & Machine Learning Bootcamp in Python

Learn Python,NumPy,Pandas,Matplotlib,Seaborn,Scikit-learn,Dask,LightGBM,XGBoost,CatBoost,Streamlit,Power BI & much more
Rating: 4.0 out of 54.0 (44 ratings)
562 students
Created by Derrick Mwiti, Namespace Labs
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

32 sections • 366 lectures • 18h 4m total length

  • Preview03:08
  • Assignment: Introduce Yourself
    00:12
  • Preview01:01
  • Preview02:16
  • About Udemy Reviews
    00:56
  • Housekeeping (Don't Skip)
    00:30

  • Preview03:20
  • Linux Launch Notebook
    00:57
  • Windows Launch Notebook
    02:10
  • Download All Notebooks
    00:02
  • Folder Structure
    00:51
  • Preview03:01
  • Python Data Types
    01:54
  • Python Lists
    04:21
  • Lists - Negative Indexing
    02:44
  • Python Dictionaries
    02:17
  • Python Tuples
    02:36
  • Python Sets
    02:18
  • Python Boolean Type
    02:08
  • Conditional Statements
    03:38
  • Python Functions
    01:41
  • Python For Loop
    02:11
  • Python While Loop
    01:55
  • Python Map Function
    01:41
  • Python Range Function
    01:34
  • Python Exercise
    00:49
  • Python Project Solutions
    09:36

  • Section Introduction
    00:15
  • Preview04:44
  • pip & virtualenv Practical
    05:34
  • Installing Packages using the Anaconda Navigator
    01:44

  • Section Introduction
    00:06
  • NumPy Introduction
    06:00
  • NumPy Arrays
    04:04
  • Checking Documentation in Notebooks
    00:37
  • Indexing One Dimensional Array
    02:27
  • Indexing Multi-dimensional Array
    07:23
  • Broadcasting in NumPy
    01:19
  • NumPy Operations
    02:47
  • NumPy Project
    00:39
  • NumPy Project Solutions
    06:07

  • Section Introduction
    00:20
  • Preview02:58
  • Pandas DataFrame
    00:59
  • Resetting the Index
    01:06
  • Deleting Columns
    00:45
  • Dealing with Null Values
    01:33
  • Creating New Columns
    00:33
  • Selecting Data in Pandas
    02:52
  • Grouping Data in Pandas
    02:29
  • Exporting a Pandas DataFrame
    01:05
  • Loading Datasets
    00:40
  • Creating Pivot Tables
    01:08
  • Pandas Project
    00:39

  • Descriptive Statistics
    01:31

  • Part 1
    02:44
  • Part 2
    00:36
  • Part 3
    01:11
  • Part 4
    01:38
  • Part 5
    02:03
  • Part 6
    01:07
  • Part 7
    02:02

  • Visualization Guide
    00:17

  • Section Introduction
    00:19
  • Matplotlib Vertical Bar Plot
    03:33
  • Matplotlib Horizontal Bar Plot
    00:48
  • Matplotlib Scatter Plot
    01:57
  • Matplotlib Histogram
    00:31
  • Matplotlib Pie Chart
    02:03
  • Matplotlib Line Plot
    00:26
  • Matplotlib Subplots
    03:04
  • Matplotlib Figure & Axes Part one
    01:33
  • Matplotlib Figure & Axes Part Two
    01:42
  • Matplotlib Project & Solutions
    00:50

  • Seaborn Count Plot
    01:25
  • Seaborn Violin Plot
    00:48
  • Seaborn - Adding Hue
    00:40
  • Seaborn Strip Plot
    00:27
  • Swarm Plot with Hue
    00:49
  • Seaborn Order X Values
    00:39
  • Strip Plot with Hue
    00:40
  • Seaborn Boxplot
    00:47
  • Seaborn Boxen Plot
    00:28
  • Seaborn Barplot
    01:19

Requirements

  • No prior programming experience required. We'll start from the basics

Description

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.

Who this course is for:

  • 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

Instructors

Derrick Mwiti
Data Scientist | Author | Mentor
Derrick Mwiti
  • 4.2 Instructor Rating
  • 55 Reviews
  • 10,031 Students
  • 3 Courses

Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community via blogs such as Heartbeat, Towards Data Science, Datacamp, Neptune AI, KDNuggets just to mention a few. Derrick is also an author and online instructor. He also trains and works with various institutions to implement data science solutions as well as to upskill their staff.

Derrick’s studied Mathematics and Computer Science from the Multimedia University, he also is an alumnus of the Meltwater Entrepreneurial School of Technology.

When Derrick is not staring at his computer, he enjoys reading and traveling

Namespace Labs
Data Science Instructor
Namespace Labs
  • 4.2 Instructor Rating
  • 55 Reviews
  • 10,031 Students
  • 3 Courses

Namespace Labs is committed to advancing the data science field by sharing the latest tools and technologies. Our bid is to create content that help new people entering the field as well as advance the skills of those in the field. We also aim at building data science solutions that eliminate redundancies, offer useful business insights, and help businesses achieve customer satisfaction through speedy, quality service delivery.

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