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Python for Machine Learning & Data Science Masterclass

Python for Machine Learning & Data Science Masterclass

Loại khoá học: Programming Languages

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

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50.000 VND
1.899.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 3 million students to learn about the future today!

What is in the course?

Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment.

We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

  • Programming with Python

  • NumPy with Python

  • Deep dive into Pandas for Data Analysis

  • Full understanding of Matplotlib Programming Library

  • Deep dive into seaborn for data visualizations

  • Machine Learning with SciKit Learn, including:

    • Linear Regression

    • Regularization

    • Lasso Regression

    • Ridge Regression

    • Elastic Net

    • K Nearest Neighbors

    • K Means Clustering

    • Decision Trees

    • Random Forests

    • Natural Language Processing

    • Support Vector Machines

    • Hierarchal Clustering

    • DBSCAN

    • PCA

    • Model Deployment

    • and much, much more!


As always, we're grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!


-Jose and Pierian Data Inc. Team

Bạn sẽ học được gì

You will learn how to use data science and machine learning with Python.

You will create data pipeline workflows to analyze, visualize, and gain insights from data.

You will build a portfolio of data science projects with real world data.

You will be able to analyze your own data sets and gain insights through data science.

Master critical data science skills.

Understand Machine Learning from top to bottom.

Replicate real-world situations and data reports.

Learn NumPy for numerical processing with Python.

Conduct feature engineering on real world case studies.

Learn Pandas for data manipulation with Python.

Create supervised machine learning algorithms to predict classes.

Learn Matplotlib to create fully customized data visualizations with Python.

Create regression machine learning algorithms for predicting continuous values.

Learn Seaborn to create beautiful statistical plots with Python.

Construct a modern portfolio of data science and machine learning resume projects.

Learn how to use Scikit-learn to apply powerful machine learning algorithms.

Get set-up quickly with the Anaconda data science stack environment.

Learn best practices for real-world data sets.

Understand the full product workflow for the machine learning lifecycle.

Explore how to deploy your machine learning models as interactive APIs.

Yêu cầu

  • Basic Python Knowledge (capable of functions)

Nội dung khoá học

26 sections

Introduction to Course

5 lectures
Welcome to the Course!
00:56
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
04:17
Anaconda Python and Jupyter Install and Setup
13:49
Note on Environment Setup - Please read me!
00:34
Environment Setup
09:08

OPTIONAL: Python Crash Course

6 lectures
OPTIONAL: Python Crash Course
00:18
Python Crash Course - Part One
16:07
Python Crash Course - Part Two
12:07
Python Crash Course - Part Three
11:19
Python Crash Course - Exercise Questions
01:29
Python Crash Course - Exercise Solutions
09:26

Machine Learning Pathway Overview

1 lectures
Machine Learning Pathway
10:16

NumPy

9 lectures
Introduction to NumPy
02:14
NumPy Arrays
22:41
Coding Exercise Check-in: Creating NumPy Arrays
1 question
NumPy Indexing and Selection
11:06
Coding Exercise Check-in: Selecting Data from Numpy Array
1 question
NumPy Operations
08:14
Check-In: Operations on NumPy Array
1 question
NumPy Exercises
01:18
Numpy Exercises - Solutions
07:05

Pandas

29 lectures
Introduction to Pandas
04:40
Series - Part One
09:28
Check-in: Labeled Index in Pandas Series
1 question
Series - Part Two
10:41
DataFrames - Part One - Creating a DataFrame
19:27
DataFrames - Part Two - Basic Properties
08:18
DataFrames - Part Three - Working with Columns
13:57
DataFrames - Part Four - Working with Rows
14:30
Pandas - Conditional Filtering
17:41
Pandas - Useful Methods - Apply on Single Column
13:47
Pandas - Useful Methods - Apply on Multiple Columns
17:23
Pandas - Useful Methods - Statistical Information and Sorting
15:48
Missing Data - Overview
11:58
Missing Data - Pandas Operations
18:32
GroupBy Operations - Part One
15:49
GroupBy Operations - Part Two - MultiIndex
14:18
Combining DataFrames - Concatenation
10:24
Combining DataFrames - Inner Merge
12:04
Combining DataFrames - Left and Right Merge
06:07
Combining DataFrames - Outer Merge
10:38
Pandas - Text Methods for String Data
16:05
Pandas - Time Methods for Date and Time Data
21:00
Pandas Input and Output - CSV Files
10:20
Pandas Input and Output - HTML Tables
14:41
Pandas Input and Output - Excel Files
07:20
Pandas Input and Output - SQL Databases
18:19
Pandas Pivot Tables
21:15
Pandas Project Exercise Overview
05:26
Pandas Project Exercise Solutions
26:31

Matplotlib

11 lectures
Introduction to Matplotlib
04:06
Matplotlib Basics
12:35
Matplotlib - Understanding the Figure Object
07:32
Matplotlib - Implementing Figures and Axes
14:31
Matplotlib - Figure Parameters
04:56
Matplotlib - Subplots Functionality
19:17
Matplotlib Styling - Legends
07:02
Matplotlib Styling - Colors and Styles
14:29
Advanced Matplotlib Commands (Optional)
03:52
Matplotlib Exercise Questions Overview
06:10
Matplotlib Exercise Questions - Solutions
16:39

Seaborn Data Visualizations

14 lectures
Introduction to Seaborn
03:54
Scatterplots with Seaborn
18:19
Distribution Plots - Part One - Understanding Plot Types
09:35
Distribution Plots - Part Two - Coding with Seaborn
16:14
Categorical Plots - Statistics within Categories - Understanding Plot Types
05:40
Categorical Plots - Statistics within Categories - Coding with Seaborn
09:15
Categorical Plots - Distributions within Categories - Understanding Plot Types
13:20
Categorical Plots - Distributions within Categories - Coding with Seaborn
17:57
Seaborn - Comparison Plots - Understanding the Plot Types
05:32
Seaborn - Comparison Plots - Coding with Seaborn
09:47
Seaborn Grid Plots
13:39
Seaborn - Matrix Plots
13:18
Seaborn Plot Exercises Overview
06:44
Seaborn Plot Exercises Solutions
14:33

Data Analysis and Visualization Capstone Project Exercise

4 lectures
Capstone Project Overview
12:48
Capstone Project Solutions - Part One
17:15
Capstone Project Solutions - Part Two
14:50
Capstone Project Solutions - Part Three
19:49

Machine Learning Concepts Overview

5 lectures
Introduction to Machine Learning Overview Section
05:13
Why Machine Learning?
09:15
Types of Machine Learning Algorithms
07:47
Supervised Machine Learning Process
13:41
Companion Book - Introduction to Statistical Learning
02:52

Linear Regression

26 lectures
Introduction to Linear Regression Section
01:39
Linear Regression - Algorithm History
09:22
Linear Regression - Understanding Ordinary Least Squares
15:43
Linear Regression - Cost Functions
08:12
Linear Regression - Gradient Descent
11:59
Python coding Simple Linear Regression
19:37
Overview of Scikit-Learn and Python
08:26
Linear Regression - Scikit-Learn Train Test Split
15:48
Linear Regression - Scikit-Learn Performance Evaluation - Regression
15:44
Linear Regression - Residual Plots
13:57
Linear Regression - Model Deployment and Coefficient Interpretation
17:46
Polynomial Regression - Theory and Motivation
07:59
Polynomial Regression - Creating Polynomial Features
10:54
Polynomial Regression - Training and Evaluation
09:44
Bias Variance Trade-Off
10:34
Polynomial Regression - Choosing Degree of Polynomial
13:37
Polynomial Regression - Model Deployment
06:07
Regularization Overview
06:39
Feature Scaling
09:59
Introduction to Cross Validation
12:53
Regularization Data Setup
08:37
L2 Regularization - Ridge Regression Theory
14:29
L2 Regularization - Ridge Regression - Python Implementation
17:42
L1 Regularization - Lasso Regression - Background and Implementation
15:01
L1 and L2 Regularization - Elastic Net
18:07
Linear Regression Project - Data Overview
04:29

Feature Engineering and Data Preparation

7 lectures
A note from Jose on Feature Engineering and Data Preparation
00:39
Introduction to Feature Engineering and Data Preparation
15:28
Dealing with Outliers
26:33
Dealing with Missing Data : Part One - Evaluation of Missing Data
10:42
Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows
20:40
Dealing with Missing Data : Part 3 - Fixing data based on Columns
23:16
Dealing with Categorical Data - Encoding Options
12:46

Cross Validation , Grid Search, and the Linear Regression Project

8 lectures
Section Overview and Introduction
03:14
Cross Validation - Test | Train Split
11:20
Cross Validation - Test | Validation | Train Split
14:48
Cross Validation - cross_val_score
11:37
Cross Validation - cross_validate
06:56
Grid Search
12:14
Linear Regression Project Overview
03:26
Linear Regression Project - Solutions
12:10

Logistic Regression

16 lectures
Early Bird Note on Downloading .zip for Logistic Regression Notes
00:16
Introduction to Logistic Regression Section
05:27
Logistic Regression - Theory and Intuition - Part One: The Logistic Function
05:36
Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic
04:54
Logistic Regression - Theory and Intuition - Linear to Logistic Math
17:00
Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood
15:42
Logistic Regression with Scikit-Learn - Part One - EDA
13:57
Logistic Regression with Scikit-Learn - Part Two - Model Training
06:38
Classification Metrics - Confusion Matrix and Accuracy
09:45
Classification Metrics - Precison, Recall, F1-Score
06:00
Classification Metrics - ROC Curves
07:13
Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation
15:56
Multi-Class Classification with Logistic Regression - Part One - Data and EDA
08:07
Multi-Class Classification with Logistic Regression - Part Two - Model
15:47
Logistic Regression Exercise Project Overview
03:58
Logistic Regression Project Exercise - Solutions
21:36

KNN - K Nearest Neighbors

6 lectures
Introduction to KNN Section
02:11
KNN Classification - Theory and Intuition
11:18
KNN Coding with Python - Part One
13:40
KNN Coding with Python - Part Two - Choosing K
23:25
KNN Classification Project Exercise Overview
03:18
KNN Classification Project Exercise Solutions
14:12

Support Vector Machines

10 lectures
Introduction to Support Vector Machines
01:29
History of Support Vector Machines
04:41
SVM - Theory and Intuition - Hyperplanes and Margins
13:25
SVM - Theory and Intuition - Kernel Intuition
04:57
SVM - Theory and Intuition - Kernel Trick and Mathematics
20:50
SVM with Scikit-Learn and Python - Classification Part One
10:59
SVM with Scikit-Learn and Python - Classification Part Two
16:02
SVM with Scikit-Learn and Python - Regression Tasks
20:59
Support Vector Machine Project Overview
04:27
Support Vector Machine Project Solutions
18:31

Tree Based Methods: Decision Tree Learning

8 lectures
Introduction to Tree Based Methods
01:22
Decision Tree - History
09:04
Decision Tree - Terminology
04:12
Decision Tree - Understanding Gini Impurity
07:52
Constructing Decision Trees with Gini Impurity - Part One
07:32
Constructing Decision Trees with Gini Impurity - Part Two
11:24
Coding Decision Trees - Part One - The Data
19:18
Coding Decision Trees - Part Two -Creating the Model
20:55

Random Forests

11 lectures
Introduction to Random Forests Section
01:46
Random Forests - History and Motivation
11:38
Random Forests - Key Hyperparameters
02:59
Random Forests - Number of Estimators and Features in Subsets
10:56
Random Forests - Bootstrapping and Out-of-Bag Error
12:46
Coding Classification with Random Forest Classifier - Part One
11:35
Coding Classification with Random Forest Classifier - Part Two
22:22
Coding Regression with Random Forest Regressor - Part One - Data
04:28
Coding Regression with Random Forest Regressor - Part Two - Basic Models
13:33
Coding Regression with Random Forest Regressor - Part Three - Polynomials
10:30
Coding Regression with Random Forest Regressor - Part Four - Advanced Models
10:36

Boosting Methods

7 lectures
Introduction to Boosting Section
01:47
Boosting Methods - Motivation and History
06:11
AdaBoost Theory and Intuition
19:51
AdaBoost Coding Part One - The Data
11:13
AdaBoost Coding Part Two - The Model
18:09
Gradient Boosting Theory
10:22
Gradient Boosting Coding Walkthrough
12:48

Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods

4 lectures
Introduction to Supervised Learning Capstone Project
14:23
Solution Walkthrough - Supervised Learning Project - Data and EDA
18:18
Solution Walkthrough - Supervised Learning Project - Cohort Analysis
23:09
Solution Walkthrough - Supervised Learning Project - Tree Models
21:23

Naive Bayes Classification and Natural Language Processing (Supervised Learning)

10 lectures
Introduction to NLP and Naive Bayes Section
02:36
Naive Bayes Algorithm - Part One - Bayes Theorem
08:04
Naive Bayes Algorithm - Part Two - Model Algorithm
17:55
Feature Extraction from Text - Part One - Theory and Intuition
10:33
Feature Extraction from Text - Coding Count Vectorization Manually
18:53
Feature Extraction from Text - Coding with Scikit-Learn
11:24
Natural Language Processing - Classification of Text - Part One
11:23
Natural Language Processing - Classification of Text - Part Two
10:18
Text Classification Project Exercise Overview
04:37
Text Classification Project Exercise Solutions
15:37

Unsupervised Learning

1 lectures
Unsupervised Learning Overview
08:17

K-Means Clustering

12 lectures
Introduction to K-Means Clustering Section
02:14
Clustering General Overview
10:36
K-Means Clustering Theory
11:30
K-Means Clustering - Coding Part One
19:48
K-Means Clustering Coding Part Two
17:18
K-Means Clustering Coding Part Three
14:31
K-Means Color Quantization - Part One
13:53
K-Means Color Quantization - Part Two
14:33
K-Means Clustering Exercise Overview
07:47
K-Means Clustering Exercise Solution - Part One
13:10
K-Means Clustering Exercise Solution - Part Two
15:51
K-Means Clustering Exercise Solution - Part Three
08:20

Hierarchical Clustering

4 lectures
Introduction to Hierarchical Clustering
00:50
Hierarchical Clustering - Theory and Intuition
11:48
Hierarchical Clustering - Coding Part One - Data and Visualization
16:12
Hierarchical Clustering - Coding Part Two - Scikit-Learn
28:22

DBSCAN - Density-based spatial clustering of applications with noise

7 lectures
Introduction to DBSCAN Section
01:00
DBSCAN - Theory and Intuition
17:26
DBSCAN versus K-Means Clustering
12:23
DBSCAN - Hyperparameter Theory
07:15
DBSCAN - Hyperparameter Tuning Methods
21:55
DBSCAN - Outlier Project Exercise Overview
05:55
DBSCAN - Outlier Project Exercise Solutions
23:20

PCA - Principal Component Analysis and Manifold Learning

7 lectures
Introduction to Principal Component Analysis
02:47
PCA Theory and Intuition - Part One
10:24
PCA Theory and Intuition - Part Two
11:12
PCA - Manual Implementation in Python
18:16
PCA - SciKit-Learn
12:09
PCA - Project Exercise Overview
07:21
PCA - Project Exercise Solution
17:03

Model Deployment

7 lectures
Model Deployment Section Overview
02:18
Model Deployment Considerations
06:50
Model Persistence
21:07
Model Deployment as an API - General Overview
07:41
Note on Upcoming Video
00:10
Model API - Creating the Script
17:00
Testing the API
07:49

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