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Python for Data Science and Machine Learning Bootcamp
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Python for Data Science and Machine Learning Bootcamp
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Software Development / Data Science

Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn, Plotly, Machine Learning, Tensorflow, and more!

Pierce Dach
Pierce Dach
8 Learners Enrolled
Intermediate
English
This course includes:
  • 18h 31m
  • 166 Lectures
  • 294 Downloadable Assets
  • Full Lifetime Access
  • Mobile and TV Access
  • Certificate on Completion
$824.00

Overview

What will students learn in your course?
  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines
What are the requirements or prerequisites for taking your course?
  • Some programming experience
  • Admin permissions to download files
Who is this course intended for?
  • This course is meant for people with at least some programming experience
Description
Course Tags

Course Content

  • 27 Sections
  • 166 Lectures
  • 18h 31m Total Length
Course Introduction
19m
3 Lectures

Course Introduction

Introduction to the Course
Preview 0:06:12
Course Help and Welcome
Preview 0:09:13
Course FAQs
0:03:41
Python Environment Setup
Preview 0:06:54
Updates to Notebook Zip
0:06:12
Jupyter Notebooks
0:06:54
Optional: Virtual Environments
0:03:41
Welcome to the Python Crash Course Section!
0:07:30
Introduction to Python Crash Course
0:06:54
Python Crash Course - Part 1
0:06:54
Python Crash Course - Part 2
0:07:30
Python Crash Course - Part 3
0:07:30
Python Crash Course - Part 4
0:06:54
Python Crash Course Exercises - Overview
0:07:30
Python Crash Course Exercises - Solutions
0:07:30
Welcome to the NumPy Section!
0:06:12
Introduction to Numpy
0:06:54
Numpy Arrays
0:09:13
Quick Note on Array Indexing
0:09:13
Numpy Array Indexing
0:07:30
Numpy Operations
0:09:13
Numpy Exercises Overview
0:06:54
Numpy Exercises Solutions
0:07:30
Welcome to the Pandas Section!
0:03:41
Introduction to Pandas
0:07:30
Series
0:03:41
DataFrames - Part 1
0:06:54
DataFrames - Part 2
0:07:30
DataFrames - Part 3
0:06:12
Missing Data
0:06:12
Groupby
0:07:30
Merging Joining and Concatenating
0:07:30
Operations
0:03:41
Data Input and Output
0:07:30
Note on SF Salary Exercise
0:06:12
SF Salaries Exercise Overview
0:06:54
SF Salaries Solutions
0:09:13
Ecommerce Purchases Exercise Overview
0:07:30
Ecommerce Purchases Exercise Solutions
0:09:13
Welcome to the Data Visualization Section!
Preview 0:06:12
Introduction to Matplotlib
Preview 0:06:54
Matplotlib Part 1
0:07:30
Matplotlib Part 2
0:07:30
Matplotlib Part 3
0:09:13
Matplotlib Exercises Overview
0:06:54
Matplotlib Exercises - Solutions
0:09:13
Introduction to Seaborn
0:07:30
Distribution Plots
Preview 0:07:30
Categorical Plots
0:07:30
Matrix Plots
0:03:41
Grids
0:06:54
Regression Plots
0:06:54
Style and Color
0:06:54
Seaborn Exercise Overview
0:07:30
Seaborn Exercise Solutions
0:09:13
Pandas Built-in Data Visualization
0:06:54
Pandas Data Visualization Exercise
0:06:12
Pandas Data Visualization Exercise- Solutions
0:06:12
Introduction to Plotly and Cufflinks
Preview 0:06:54
READ ME FIRST BEFORE PLOTLY PLEASE!
0:07:30
Plotly and Cufflinks
0:07:30
Introduction to Geographical Plotting
Preview 0:06:54
Choropleth Maps - Part 1 - USA
0:03:41
Choropleth Maps - Part 2 - World
0:06:12
Choropleth Exercises
0:06:12
Choropleth Exercises - Solutions
0:06:12
Welcome to the Data Capstone Projects!
Preview 0:06:54
911 Calls Project Overview
Preview 0:07:30
911 Calls Solutions - Part 1
0:03:41
911 Calls Solutions - Part 2
0:03:41
Bank Data
0:07:30
Finance Data Project Overview
0:07:30
Finance Project - Solutions Part 1
0:06:12
Finance Project - Solutions Part 2
0:07:30
Finance Project - Solutions Part 3
0:06:12
Welcome to Machine Learning. Here are a few resources to get you started!
0:06:54
Welcome to the Machine Learning Section!
0:07:30
Supervised Learning Overview
0:07:30
Evaluating Performance - Classification Error Metrics
0:09:13
Evaluating Performance - Regression Error Metrics
0:06:54
Machine Learning with Python
0:09:13
Linear Regression Theory
0:03:41
model_selection Updates for SciKit Learn 0.18
0:06:12
Linear Regression with Python - Part 1
0:07:30
Linear Regression with Python - Part 2
0:06:12
Linear Regression Project Overview
0:09:13
Linear Regression Project Solution
0:06:54
Bias Variance Trade-Off
0:09:13
Logistic Regression Theory
0:03:41
Logistic Regression with Python - Part 1
0:07:30
Logistic Regression with Python - Part 2
0:07:30
Logistic Regression with Python - Part 3
0:03:41
Logistic Regression Project Overview
0:06:12
Logistic Regression Project Solutions
0:09:13
KNN Theory
0:03:41
KNN with Python
0:09:13
KNN Project Overview
0:09:13
KNN Project Solutions
0:03:41
Introduction to Tree Methods
0:07:30
Decision Trees and Random Forest with Python
0:07:30
Decision Trees and Random Forest Project Overview
0:07:30
Decision Trees and Random Forest Solutions Part 1
0:06:12
Decision Trees and Random Forest Solutions Part 2
0:03:41
SVM Theory
0:06:54
Support Vector Machines with Python
0:06:12
SVM Project Overview
0:03:41
SVM Project Solutions
0:06:12
K Means Algorithm Theory
0:06:12
K Means with Python
0:06:54
K Means Project Overview
0:03:41
K Means Project Solutions
0:06:12
Principal Component Analysis
0:03:41
PCA with Python
0:07:30
Recommender Systems
0:06:54
Recommender Systems with Python - Part 1
0:06:54
Recommender Systems with Python - Part 2
0:07:30
Natural Language Processing Theory
0:03:41
NLP with Python - Part 1
0:06:12
NLP with Python - Part 2
0:09:13
NLP with Python - Part 3
0:07:30
NLP Project Overview
0:07:30
NLP Project Solutions
0:03:41
Download TensorFlow Notebooks Here
0:03:41
Quick Check for Notes
0:06:12
Welcome to the Deep Learning Section!
0:06:54
Introduction to Artificial Neural Networks (ANN)
0:06:12
Installing Tensorflow
0:06:54
Perceptron Model
0:03:41
Neural Networks
0:07:30
Activation Functions
0:06:12
Multi-Class Classification Considerations
0:03:41
Cost Functions and Gradient Descent
0:06:54
Backpropagation
0:06:12
TensorFlow vs Keras
0:06:12
TF Syntax Basics - Part One - Preparing the Data
0:07:30
TF Syntax Basics - Part Two - Creating and Training the Model
0:06:54
TF Syntax Basics - Part Three - Model Evaluation
0:06:12
TF Regression Code Along - Exploratory Data Analysis
0:06:54
TF Regression Code Along - Exploratory Data Analysis - Continued
0:06:12
TF Regression Code Along - Data Preprocessing and Creating a Model
0:07:30
TF Regression Code Along - Model Evaluation and Predictions
0:07:30
TF Classification Code Along - EDA and Preprocessing
0:07:30
TF Classification - Dealing with Overfitting and Evaluation
0:06:54
TensorFlow 2.0 Project Options Overview
0:07:30
TensorFlow 2.0 Project Notebook Overview
0:06:12
Keras Project Solutions - Dealing with Missing Data
0:06:12
Keras Project Solutions - Dealing with Missing Data - Part Two
0:06:54
Keras Project Solutions - Categorical Data
0:07:30
Keras Project Solutions - Data PreProcessing
0:09:13
Keras Project Solutions - Data PreProcessing
0:07:30
Keras Project Solutions - Creating and Training a Model
0:06:12
Keras Project Solutions - Model Evaluation
0:03:41
Tensorboard
0:06:12
Welcome to the Big Data Section!
0:06:54
Big Data Overview
0:06:54
Spark Overview
0:09:13
Local Spark Set-Up
0:07:30
AWS Account Set-Up
0:03:41
Quick Note on AWS Security
0:06:54
EC2 Instance Set-Up
0:03:41
SSH with Mac or Linux
0:06:12
PySpark Setup
0:06:54
Lambda Expressions Review
0:07:30
Introduction to Spark and Python
0:07:30
RDD Transformations and Actions
0:06:12
Bonus Lecture
0:09:13

About the Coach

Pierce Dach
0.00 (0)

Pierce Dach

Courses 3

Hello everyone! I am Pierce Dach and I've worked in learning and skills service for above 8 years. The goal of learning english is to enhance fluency by improving vocabulary and structure. I will make learning english more convenient for you.

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