Data Science And Machine Learning Fundamentals I

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 and execute common Data Science and Machine Learning tasks with confidence

Basic Python and Pandas for data handling, and knowledge about Scikit-learn, Statsmodels, Matplotlib, Seaborn, and other libraries

Detailed knowledge about Regression Prediction, Classification, and Cluster analysis

Requirements

The sole requirements are the four ways of counting (+-*/) and everyday experience with Windows, or 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 fastest-developing areas right now. This course will teach you the fundamentals of Data Science and Machine Learning, and has content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist.You will learn aboutRegression and Prediction with Machine Learning models using supervised learning. This course has one of the most complete and fundamental content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building and feature selection, and artificial intelligence. You will learn about models ranging from simple linear regression models to more 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.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 7 useful Machine Learning cluster algorithms. The fundamentals of Data Science and Machine Learning.Basic Python and Pandas for data handling.This course includesa comprehensive and easy-to-follow teaching package for basic Python and Pandas for data handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Data Science, or Machine Learning.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 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 a Ph.D. or no education or experience at all.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 and execute common Data Science and Machine Learning tasks with confidence.Basic Python and Pandas for data handling, and have knowledge about Scikit-learn, Statsmodels, Matplotlib, Seaborn, and other libraries.Detailed knowledge about Regression Prediction, Classification, and Cluster Analysis.

Overview

Section 1: Introduction

Lecture 1 Course introduction

Lecture 2 Download and installation of the Anaconda Distribution plus Visual Studio Code

Lecture 3 Setup of Anaconda Distribution for this course

Section 2: Basic Python and Pandas for data handling

Lecture 4 Overview plus integers, floats, and strings

Lecture 5 Set, Tuple, Dictionary, and Lists

Lecture 6 Data Transformers and Functions

Lecture 7 Introduction to Pandas, the DataFrame

Lecture 8 Pandas, loading and saving files

Lecture 9 Pandas operations and techniques. Select, Extract, and Slice

Lecture 10 Pandas: Data cleaning, preparation, description, and visualization

Section 3: Regression and Prediction with Machine Learning models

Lecture 11 Regression and Supervised Learning. Overview, and assumptions to diagnostics

Lecture 12 Regression and Prediction, some practical modelling concepts

Lecture 13 Linear Multiple Regression model. Model building and automatic forward selection

Lecture 14 Multivariate Polynomial Regression models and automatic feature selection

Lecture 15 Regression Regularization, Lasso and Ridge models

Lecture 16 Decision Tree Regression models

Lecture 17 Random Forest Regression

Section 4: Classification with Machine Learning models

Lecture 18 Classification, overview, the classification process, and theory

Lecture 19 Logistic Regression for Classification

Lecture 20 The Naive Bayes Classifier

Lecture 21 The Decision Tree Classifier and visualizations of Tree Classifiers

Lecture 22 The Random Forest Classifier

Section 5: Cluster Analysis and Unsupervised Learning with Machine Learning models

Lecture 23 Cluster Analysis, overview and pedagogy

Lecture 24 K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms

Lecture 25 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Lecture 26 Four Hierarchical Clustering algorithms

Section 6: Advanced Machine Learning models and tasks

Lecture 27 Overview

Lecture 28 Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron

Lecture 29 Feedforward Multi-Layer Perceptrons for Classification tasks

Lecture 30 Feedforward Multi-Layer Perceptrons for Prediction tasks

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.