Learn to master Data Science and Machine Learning Fundamentals with Python and Pandas

What you'll learn

Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks

Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks

The ability to handle common Data Science and Machine Learning tasks with confidence

Master Python for Data Handling

Master Pandas for Data Handling

Knowledge and practical hands-on knowledge of Scikit-learn, Stats models, Matplotlib, Seaborn, and many other Python libraries

Detailed and deep, Master knowledge of Regression Prediction, Classification, and Cluster analysis

Advanced knowledge of A.I. prediction models and automatic model creation

Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining

Requirements

The four ways of counting (+-*/)

Everyday experience with Windows, Linux, or Mac-OS

Description

This course is an exciting hands-on view of the fundamentals of Data Science and Machine LearningData Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now. This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value. You will learn aboutRegression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.The fundamentals of Data Science and Machine Learning. This course gives a very solid foundation and knowledge base for Data Science and Machine Learning jobs or studies.Advanced A.I. prediction models and automatic model creation. This video course includes videos where the use of very powerful algorithms for automatic model creation is taught.Advanced Text Mining and Automation. You will learn to mine text data and the fundamentals of Text and Emotion Mining such as Tokenization, text data preparation, spell checking, lemmatization, stemming, and classification of text data. Mastering Python for data handling.Mastering Pandas for data handling.This course includesa comprehensive and easy-to-follow teaching package for Mastering Python and Pandas for data handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Pandas, Data Science, or Machine Learning.an optional possibility to use the Anaconda Cloud Notebook for cloud computing.an easy-to-follow guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install the Python Data Science and Machine Learning environment for this course.content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist.a large collection of unique content, and will teach you many new things that only can be learned from this course on Udemy.A complete masterclass package for Data Science and Machine Learning.A course structure built on a proven and professional framework for learning.A compact course structure and no killing time.Is this course for you?This course is for you, regardless if you are a beginner or an experienced Data Scientist. This course is for you, regardless if you have no education or are experienced with a Ph.D.Course requirementsThe four ways of counting (+-*/)Basic everyday experience with either Windows, Linux, Mac OS, or similar operating systemsAfter completing this course, you will haveKnowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks.Deep hands-on knowledge of Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks.The ability to handle common Data Science and Machine Learning tasks with confidence.Knowledge to Master Python for Data Handling.Knowledge to Master Pandas for Data Handling.Knowledge and practical hands-on knowledge of Scikit-learn, Stats models, Matplotlib, Seaborn, and many other Python libraries.Detailed and deep Master knowledge of Regression Prediction, Classification, and Cluster Analysis.Advanced knowledge of A.I. prediction models and automatic model creation.Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining.

Overview

Section 1: Introduction

Lecture 1 Course introduction

Lecture 2 Workplace Setup with options

Lecture 3 Setup of the Anaconda Jupyter Cloud Notebook

Lecture 4 Download and installation of the Anaconda Distribution plus Visual Studio Code

Lecture 5 Setup of Anaconda Distribution with libraries in a pre-designed environment

Lecture 6 Setup of Anaconda Distribution with libraries in the base/root environment

Lecture 7 Setup of Anaconda Distribution with libraries in a working environment

Section 2: Master Python for data handling

Lecture 8 Overview of the first part of this section

Lecture 9 Python Integers

Lecture 10 Python Floats

Lecture 11 Python Strings I

Lecture 12 Python Strings II: Intermediate String Methods

Lecture 13 Python Strings III: DateTime Objects and Strings

Lecture 14 Python Native Data Storage Overview

Lecture 15 Python Set

Lecture 16 Python Tuple

Lecture 17 Python Dictionary

Lecture 18 Python List

Lecture 19 Data Transformers and Functions

Lecture 20 The While Loop

Lecture 21 The For Loop

Lecture 22 Python Logic Operators

Lecture 23 Python Functions I

Lecture 24 Python Functions II

Lecture 25 Python Object Oriented Programming I : Theory

Lecture 26 Python Object Oriented Programming II: OOP

Lecture 27 Python Object Oriented Programming III: Files and Tables

Lecture 28 Python Object Oriented Programming IV: Recap and More

Section 3: Master Pandas for Data Handling

Lecture 29 Master Pandas for Data Handling: Overview

Lecture 30 Pandas theory and terminology

Lecture 31 Creating a DataFrame from scratch

Lecture 32 Pandas File Handling: Overview

Lecture 33 Pandas File Handling: The .csv file format

Lecture 34 Pandas File Handling: The .xlsx file format

Lecture 35 Pandas File Handling: SQL-database files

Lecture 36 Pandas Operations & Techniques: Overview

Lecture 37 Pandas Operations & Techniques: Object Inspection

Lecture 38 Pandas Operations & Techniques: DataFrame Inspection

Lecture 39 Pandas Operations & Techniques: Column Selections

Lecture 40 Pandas Operations & Techniques: Row Selections

Lecture 41 Pandas Operations & Techniques: Conditional Selections

Lecture 42 Pandas Operations & Techniques: Scalers and Standardization.

Lecture 43 Pandas Operations & Techniques: Concatenate DataFrames

Lecture 44 Pandas Operations & Techniques: Joining DataFrames

Lecture 45 Pandas Operations & Techniques: Merging DataFrames

Lecture 46 Pandas Operations & Techniques: Transpose & Pivot Functions

Lecture 47 Pandas Data Preparation I: Overview & workflow

Lecture 48 Pandas Data Preparation II: Edit DataFrame labels

Lecture 49 Pandas Data Preparation III: Duplicates

Lecture 50 Pandas Data Preparation IV: Missing Data & Imputation

Lecture 51 Pandas Data Preparation V: Data Binnings [Extra Video]

Lecture 52 Pandas Data Preparation VI: Indicator Features [Extra Video]

Lecture 53 Pandas Data Description I: Overview

Lecture 54 Pandas Data Description II: Sorting and Ranking

Lecture 55 Pandas Data Description III: Descriptive Statistics

Lecture 56 Pandas Data Description IV: Crosstabulations & Groupings

Lecture 57 Pandas Data Visualization I: Overview

Lecture 58 Pandas Data Visualization II: Histograms

Lecture 59 Pandas Data Visualization III: Boxplots

Lecture 60 Pandas Data Visualization IV: Scatterplots

Lecture 61 Pandas Data Visualization V: Pie Charts

Lecture 62 Pandas Data Visualization VI: Line plots

Section 4: Regression and Prediction with Machine Learning models

Lecture 63 Regression, Prediction, and Supervised Learning. Section Overview (I)

Lecture 64 The Traditional Simple Regression Model (II)

Lecture 65 The Traditional Simple Regression Model (III)

Lecture 66 Some practical and useful modelling concepts (IV)

Lecture 67 Some practical and useful modelling concepts (V)

Lecture 68 Linear Multiple Regression model (VI)

Lecture 69 Linear Multiple Regression model (VII)

Lecture 70 Multivariate Polynomial Multiple Regression models (VIII)

Lecture 71 Multivariate Polynomial Multiple Regression models (VIIII)

Lecture 72 Regression Regularization, Lasso and Ridge models (X)

Lecture 73 Decision Tree Regression models (XI)

Lecture 74 Random Forest Regression (XII)

Lecture 75 Voting Regression (XIII)

Section 5: Classification with Machine Learning models

Lecture 76 Classification and Supervised Learning, overview

Lecture 77 Logistic Regression Classifier

Lecture 78 The Naive Bayes Classifier

Lecture 79 The Decision Tree Classifier

Lecture 80 The Random Forest Classifier

Lecture 81 Linear Discriminant Analysis (LDA) [Extra Video]

Lecture 82 The Voting Classifier

Section 6: Cluster Analysis and Unsupervised Learning

Lecture 83 Cluster Analysis, an overview

Lecture 84 K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms

Lecture 85 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Lecture 86 Four Hierarchical Clustering algorithms

Section 7: Advanced Machine Learning models and tasks

Lecture 87 Overview

Lecture 88 Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron

Lecture 89 Feedforward Multi-Layer Perceptrons for Classification tasks

Lecture 90 Feedforward Multi-Layer Perceptrons for Prediction tasks

Section 8: Text Mining and NLP

Lecture 91 Text Mining and NLP introduction

Lecture 92 Text Mining Tasks

Lecture 93 Text Mining Process

Lecture 94 Text Indexing Process

Lecture 95 The Tokenization Process

Lecture 96 Spelling correction and stop words

Lecture 97 Lemmatization and Stemming

Lecture 98 The Bag of Words Data Structure and some models

Lecture 99 The TF-IDF Data Structure and some models

Lecture 100 The N-grams Data Structure

Lecture 101 Attention-based models and Generative Pre-trained Transformer models

Lecture 102 Emotion Mining and Sentiment Analysis

This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.