Data Science And Machine Learning Fundamentals [Theory Only]

Data Science And Machine Learning Fundamentals [Theory Only]
Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Theorical Course for Data Science, Machine Learning, Deep Learning to understand the logic of Data Science algorithms

What you’ll learn

What is Machine Learning?

Machine Learning Terminology

Evaluation Metrics

What are Classification vs Regression?

Evaluating Performance-Classification Error Metrics

Evaluating Performance-Regression Error Metrics

Supervised Learning

Linear Regression Algorithm

Logistic Regresion Algorithm

K Nearest Neighbors Algorithm

Decision Trees And Random Forest Algorithm

Support Vector Machine Algorithm

Unsupervised Learning

K Means Clustering Algorithm

Hierarchical Clustering Algorithm

Principal Component Analysis (PCA)

Recommender System Algorithm

Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.

Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.

Machine learning describes systems that make predictions using a model trained on real-world data.

Requirements

Basic knowledge of Python Programming Language

Be Able To Operate & Install Software On A Computer

Free software and tools used during the machine learning a-z course

Free software and tools used during the machine learning a-z course

Determination to learn machine learning and patience.

Motivation to learn the the second largest number of job postings relative program language among all others

Curiosity for machine learning python

Desire to work on python machinace learning

Desire to learn machine learning a-z, complete machine learning

Any device you can watch the course, such as a mobile phone, computer or tablet.

Watching the lecture videos completely, to the end and in order.

LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device.

Description

Overview

Section 1: Introduction to Machine Learning?

Lecture 1 What is Machine Learning?

Lecture 2 What are Machine Learning Terminologies?

Section 2: Evaluation Metrics in Machine Learning

Lecture 3 Classification vs Regression in Machine Learning

Lecture 4 Evaluating Performance: Classification Error Metrics

Lecture 5 Evaluating Performance: Regression Error Metrics

Section 3: Supervised Learning with Machine Learning

Lecture 6 What is Supervised Learning in Machine Learning?

Section 4: Supervised Learning Algorithms

Lecture 7 Linear Regression Algorithm Theory

Lecture 9 Logistic Regression Algorithm Theory

Lecture 10 K-Fold Cross-Validation Theory

Lecture 11 Hyperparameter Optimization Theory

Lecture 12 K Nearest Neighbors Algorithm Theory

Lecture 13 Decision Tree Algorithm Theory

Lecture 14 Random Forest Algorithm Theory

Lecture 15 Support Vector Machine Algorithm Theory

Section 5: Unsupervised Learning with Machine Learning

Lecture 16 What is unsupervised Learning in Machine Learning?

Section 6: Unsupervised Learning Algorithms

Lecture 17 K Means Clustering Algorithm Theory

Lecture 18 Hierarchical Clustering Algorithm Theory

Lecture 19 Principal Component Analysis (PCA) Theory

Section 7: Extra

Lecture 22 Data Science and Machine Learning Fundamentals [Theory Only]