## 2022 Python for Machine Learning Data Science Masterclass

### What you’ll learn

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.

### Requirements

Basic Python Knowledge (capable of functions)

### Description

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla’s over 2.6 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 PythonNumPy with PythonDeep dive into Pandas for Data AnalysisFull understanding of Matplotlib Programming LibraryDeep dive into seaborn for data visualizationsMachine Learning with SciKit Learn, including:Linear RegressionRegularizationLasso RegressionRidge RegressionElastic NetK Nearest NeighborsK Means ClusteringDecision TreesRandom ForestsNatural Language ProcessingSupport Vector MachinesHierarchal ClusteringDBSCANPCAModel Deploymentand 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

### Overview

Section 1: Introduction to Course

Lecture 1 Welcome to the Course!

Lecture 2 COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

Lecture 3 Anaconda Python and Jupyter Install and Setup

Lecture 4 Note on Environment Setup – Please read me!

Lecture 5 Environment Setup

Section 2: OPTIONAL: Python Crash Course

Lecture 6 OPTIONAL: Python Crash Course

Lecture 7 Python Crash Course – Part One

Lecture 8 Python Crash Course – Part Two

Lecture 9 Python Crash Course – Part Three

Lecture 10 Python Crash Course – Exercise Questions

Lecture 11 Python Crash Course – Exercise Solutions

Section 3: Machine Learning Pathway Overview

Lecture 12 Machine Learning Pathway

Section 4: NumPy

Lecture 13 Introduction to NumPy

Lecture 14 NumPy Arrays

Lecture 15 NumPy Indexing and Selection

Lecture 16 NumPy Operations

Lecture 17 NumPy Exercises

Lecture 18 Numpy Exercises – Solutions

Section 5: Pandas

Lecture 19 Introduction to Pandas

Lecture 20 Series – Part One

Lecture 21 Series – Part Two

Lecture 22 DataFrames – Part One – Creating a DataFrame

Lecture 23 DataFrames – Part Two – Basic Properties

Lecture 24 DataFrames – Part Three – Working with Columns

Lecture 25 DataFrames – Part Four – Working with Rows

Lecture 26 Pandas – Conditional Filtering

Lecture 27 Pandas – Useful Methods – Apply on Single Column

Lecture 28 Pandas – Useful Methods – Apply on Multiple Columns

Lecture 29 Pandas – Useful Methods – Statistical Information and Sorting

Lecture 30 Missing Data – Overview

Lecture 31 Missing Data – Pandas Operations

Lecture 32 GroupBy Operations – Part One

Lecture 33 GroupBy Operations – Part Two – MultiIndex

Lecture 34 Combining DataFrames – Concatenation

Lecture 35 Combining DataFrames – Inner Merge

Lecture 36 Combining DataFrames – Left and Right Merge

Lecture 37 Combining DataFrames – Outer Merge

Lecture 38 Pandas – Text Methods for String Data

Lecture 39 Pandas – Time Methods for Date and Time Data

Lecture 40 Pandas Input and Output – CSV Files

Lecture 41 Pandas Input and Output – HTML Tables

Lecture 42 Pandas Input and Output – Excel Files

Lecture 43 Pandas Input and Output – SQL Databases

Lecture 44 Pandas Pivot Tables

Lecture 45 Pandas Project Exercise Overview

Lecture 46 Pandas Project Exercise Solutions

Section 6: Matplotlib

Lecture 47 Introduction to Matplotlib

Lecture 48 Matplotlib Basics

Lecture 49 Matplotlib – Understanding the Figure Object

Lecture 50 Matplotlib – Implementing Figures and Axes

Lecture 51 Matplotlib – Figure Parameters

Lecture 52 Matplotlib – Subplots Functionality

Lecture 53 Matplotlib Styling – Legends

Lecture 54 Matplotlib Styling – Colors and Styles

Lecture 55 Advanced Matplotlib Commands (Optional)

Lecture 56 Matplotlib Exercise Questions Overview

Lecture 57 Matplotlib Exercise Questions – Solutions

Section 7: Seaborn Data Visualizations

Lecture 58 Introduction to Seaborn

Lecture 59 Scatterplots with Seaborn

Lecture 60 Distribution Plots – Part One – Understanding Plot Types

Lecture 61 Distribution Plots – Part Two – Coding with Seaborn

Lecture 62 Categorical Plots – Statistics within Categories – Understanding Plot Types

Lecture 63 Categorical Plots – Statistics within Categories – Coding with Seaborn

Lecture 64 Categorical Plots – Distributions within Categories – Understanding Plot Types

Lecture 65 Categorical Plots – Distributions within Categories – Coding with Seaborn

Lecture 66 Seaborn – Comparison Plots – Understanding the Plot Types

Lecture 67 Seaborn – Comparison Plots – Coding with Seaborn

Lecture 68 Seaborn Grid Plots

Lecture 69 Seaborn – Matrix Plots

Lecture 70 Seaborn Plot Exercises Overview

Lecture 71 Seaborn Plot Exercises Solutions

Section 8: Data Analysis and Visualization Capstone Project Exercise

Lecture 72 Capstone Project Overview

Lecture 73 Capstone Project Solutions – Part One

Lecture 74 Capstone Project Solutions – Part Two

Lecture 75 Capstone Project Solutions – Part Three

Section 9: Machine Learning Concepts Overview

Lecture 76 Introduction to Machine Learning Overview Section

Lecture 77 Why Machine Learning?

Lecture 78 Types of Machine Learning Algorithms

Lecture 79 Supervised Machine Learning Process

Lecture 80 Companion Book – Introduction to Statistical Learning

Section 10: Linear Regression

Lecture 81 Introduction to Linear Regression Section

Lecture 82 Linear Regression – Algorithm History

Lecture 83 Linear Regression – Understanding Ordinary Least Squares

Lecture 84 Linear Regression – Cost Functions

Lecture 85 Linear Regression – Gradient Descent

Lecture 86 Python coding Simple Linear Regression

Lecture 87 Overview of Scikit-Learn and Python

Lecture 88 Linear Regression – Scikit-Learn Train Test Split

Lecture 89 Linear Regression – Scikit-Learn Performance Evaluation – Regression

Lecture 90 Linear Regression – Residual Plots

Lecture 91 Linear Regression – Model Deployment and Coefficient Interpretation

Lecture 92 Polynomial Regression – Theory and Motivation

Lecture 93 Polynomial Regression – Creating Polynomial Features

Lecture 94 Polynomial Regression – Training and Evaluation

Lecture 95 Bias Variance Trade-Off

Lecture 96 Polynomial Regression – Choosing Degree of Polynomial

Lecture 97 Polynomial Regression – Model Deployment

Lecture 98 Regularization Overview

Lecture 99 Feature Scaling

Lecture 100 Introduction to Cross Validation

Lecture 101 Regularization Data Setup

Lecture 102 L2 Regularization – Ridge Regression Theory

Lecture 103 L2 Regularization – Ridge Regression – Python Implementation

Lecture 104 L1 Regularization – Lasso Regression – Background and Implementation

Lecture 105 L1 and L2 Regularization – Elastic Net

Lecture 106 Linear Regression Project – Data Overview

Section 11: Feature Engineering and Data Preparation

Lecture 107 A note from Jose on Feature Engineering and Data Preparation

Lecture 108 Introduction to Feature Engineering and Data Preparation

Lecture 109 Dealing with Outliers

Lecture 110 Dealing with Missing Data : Part One – Evaluation of Missing Data

Lecture 111 Dealing with Missing Data : Part Two – Filling or Dropping data based on Rows

Lecture 112 Dealing with Missing Data : Part 3 – Fixing data based on Columns

Lecture 113 Dealing with Categorical Data – Encoding Options

Section 12: Cross Validation , Grid Search, and the Linear Regression Project

Lecture 114 Section Overview and Introduction

Lecture 115 Cross Validation – Test | Train Split

Lecture 116 Cross Validation – Test | Validation | Train Split

Lecture 117 Cross Validation – cross_val_score

Lecture 118 Cross Validation – cross_validate

Lecture 119 Grid Search

Lecture 120 Linear Regression Project Overview

Lecture 121 Linear Regression Project – Solutions

Section 13: Logistic Regression

Lecture 122 Early Bird Note on Downloading .zip for Logistic Regression Notes

Lecture 123 Introduction to Logistic Regression Section

Lecture 124 Logistic Regression – Theory and Intuition – Part One: The Logistic Function

Lecture 125 Logistic Regression – Theory and Intuition – Part Two: Linear to Logistic

Lecture 126 Logistic Regression – Theory and Intuition – Linear to Logistic Math

Lecture 127 Logistic Regression – Theory and Intuition – Best fit with Maximum Likelihood

Lecture 128 Logistic Regression with Scikit-Learn – Part One – EDA

Lecture 129 Logistic Regression with Scikit-Learn – Part Two – Model Training

Lecture 130 Classification Metrics – Confusion Matrix and Accuracy

Lecture 131 Classification Metrics – Precison, Recall, F1-Score

Lecture 132 Classification Metrics – ROC Curves

Lecture 133 Logistic Regression with Scikit-Learn – Part Three – Performance Evaluation

Lecture 134 Multi-Class Classification with Logistic Regression – Part One – Data and EDA

Lecture 135 Multi-Class Classification with Logistic Regression – Part Two – Model

Lecture 136 Logistic Regression Exercise Project Overview

Lecture 137 Logistic Regression Project Exercise – Solutions

Section 14: KNN – K Nearest Neighbors

Lecture 138 Introduction to KNN Section

Lecture 139 KNN Classification – Theory and Intuition

Lecture 140 KNN Coding with Python – Part One

Lecture 141 KNN Coding with Python – Part Two – Choosing K

Lecture 142 KNN Classification Project Exercise Overview

Lecture 143 KNN Classification Project Exercise Solutions

Section 15: Support Vector Machines

Lecture 144 Introduction to Support Vector Machines

Lecture 145 History of Support Vector Machines

Lecture 146 SVM – Theory and Intuition – Hyperplanes and Margins

Lecture 147 SVM – Theory and Intuition – Kernel Intuition

Lecture 148 SVM – Theory and Intuition – Kernel Trick and Mathematics

Lecture 149 SVM with Scikit-Learn and Python – Classification Part One

Lecture 150 SVM with Scikit-Learn and Python – Classification Part Two

Lecture 151 SVM with Scikit-Learn and Python – Regression Tasks

Lecture 152 Support Vector Machine Project Overview

Lecture 153 Support Vector Machine Project Solutions

Section 16: Tree Based Methods: Decision Tree Learning

Lecture 154 Introduction to Tree Based Methods

Lecture 155 Decision Tree – History

Lecture 156 Decision Tree – Terminology

Lecture 157 Decision Tree – Understanding Gini Impurity

Lecture 158 Constructing Decision Trees with Gini Impurity – Part One

Lecture 159 Constructing Decision Trees with Gini Impurity – Part Two

Lecture 160 Coding Decision Trees – Part One – The Data

Lecture 161 Coding Decision Trees – Part Two -Creating the Model

Section 17: Random Forests

Lecture 162 Introduction to Random Forests Section

Lecture 163 Random Forests – History and Motivation

Lecture 164 Random Forests – Key Hyperparameters

Lecture 165 Random Forests – Number of Estimators and Features in Subsets

Lecture 166 Random Forests – Bootstrapping and Out-of-Bag Error

Lecture 167 Coding Classification with Random Forest Classifier – Part One

Lecture 168 Coding Classification with Random Forest Classifier – Part Two

Lecture 169 Coding Regression with Random Forest Regressor – Part One – Data

Lecture 170 Coding Regression with Random Forest Regressor – Part Two – Basic Models

Lecture 171 Coding Regression with Random Forest Regressor – Part Three – Polynomials

Lecture 172 Coding Regression with Random Forest Regressor – Part Four – Advanced Models

Section 18: Boosting Methods

Lecture 173 Introduction to Boosting Section

Lecture 174 Boosting Methods – Motivation and History

Lecture 175 AdaBoost Theory and Intuition

Lecture 176 AdaBoost Coding Part One – The Data

Lecture 177 AdaBoost Coding Part Two – The Model

Lecture 178 Gradient Boosting Theory

Lecture 179 Gradient Boosting Coding Walkthrough

Section 19: Supervised Learning Capstone Project – Cohort Analysis and Tree Based Methods

Lecture 180 Introduction to Supervised Learning Capstone Project

Lecture 181 Solution Walkthrough – Supervised Learning Project – Data and EDA

Lecture 182 Solution Walkthrough – Supervised Learning Project – Cohort Analysis

Lecture 183 Solution Walkthrough – Supervised Learning Project – Tree Models

Section 20: Naive Bayes Classification and Natural Language Processing (Supervised Learning)

Lecture 184 Introduction to NLP and Naive Bayes Section

Lecture 185 Naive Bayes Algorithm – Part One – Bayes Theorem

Lecture 186 Naive Bayes Algorithm – Part Two – Model Algorithm

Lecture 187 Feature Extraction from Text – Part One – Theory and Intuition

Lecture 188 Feature Extraction from Text – Coding Count Vectorization Manually

Lecture 189 Feature Extraction from Text – Coding with Scikit-Learn

Lecture 190 Natural Language Processing – Classification of Text – Part One

Lecture 191 Natural Language Processing – Classification of Text – Part Two

Lecture 192 Text Classification Project Exercise Overview

Lecture 193 Text Classification Project Exercise Solutions

Section 21: Unsupervised Learning

Lecture 194 Unsupervised Learning Overview

Section 22: K-Means Clustering

Lecture 195 Introduction to K-Means Clustering Section

Lecture 196 Clustering General Overview

Lecture 197 K-Means Clustering Theory

Lecture 198 K-Means Clustering – Coding Part One

Lecture 199 K-Means Clustering Coding Part Two

Lecture 200 K-Means Clustering Coding Part Three

Lecture 201 K-Means Color Quantization – Part One

Lecture 202 K-Means Color Quantization – Part Two

Lecture 203 K-Means Clustering Exercise Overview

Lecture 204 K-Means Clustering Exercise Solution – Part One

Lecture 205 K-Means Clustering Exercise Solution – Part Two

Lecture 206 K-Means Clustering Exercise Solution – Part Three

Section 23: Hierarchical Clustering

Lecture 207 Introduction to Hierarchical Clustering

Lecture 208 Hierarchical Clustering – Theory and Intuition

Lecture 209 Hierarchical Clustering – Coding Part One – Data and Visualization

Lecture 210 Hierarchical Clustering – Coding Part Two – Scikit-Learn

Section 24: DBSCAN – Density-based spatial clustering of applications with noise

Lecture 211 Introduction to DBSCAN Section

Lecture 212 DBSCAN – Theory and Intuition

Lecture 213 DBSCAN versus K-Means Clustering

Lecture 214 DBSCAN – Hyperparameter Theory

Lecture 215 DBSCAN – Hyperparameter Tuning Methods

Lecture 216 DBSCAN – Outlier Project Exercise Overview

Lecture 217 DBSCAN – Outlier Project Exercise Solutions

Section 25: PCA – Principal Component Analysis and Manifold Learning

Lecture 218 Introduction to Principal Component Analysis

Lecture 219 PCA Theory and Intuition – Part One

Lecture 220 PCA Theory and Intuition – Part Two

Lecture 221 PCA – Manual Implementation in Python

Lecture 222 PCA – SciKit-Learn

Lecture 223 PCA – Project Exercise Overview

Lecture 224 PCA – Project Exercise Solution

Section 26: Model Deployment

Lecture 225 Model Deployment Section Overview

Lecture 226 Model Deployment Considerations

Lecture 227 Model Persistence

Lecture 228 Model Deployment as an API – General Overview

Lecture 229 Note on Upcoming Video

Lecture 230 Model API – Creating the Script

Lecture 231 Testing the API

Beginner Python developers curious about Machine Learning and Data Science with Python

#### Course Information:

Udemy | English | 44h 4m | 16.31 GB

Created by: Jose Portilla

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