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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