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Machine Learning Course For Absolute Beginners by Ankit Srivastava
Unlock the power of Machine Learning! Learn supervised, unsupervised and reinforcement learning with hands-on examples
What you'll learn
Supervised Machine Learning Algorithms and examples
Unsupervised Machine Learning Algorithms and examples
Reinforcement Algorithms and examples
Requirements
Basic understanding of Python Programming Language
Description
Are you curious about Machine Learning but have no prior experience? This course is perfect for you! Designed specifically for beginners, we break down the complexities of Machine Learning into simple, easy-to-understand concepts.Through real-world examples and practical exercises, you'll explore the foundations of supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, a professional looking to upskill, or simply a tech enthusiast, this course will provide you with the skills to kickstart your Machine Learning journey.Learn how to perform Exploratory Data Analysis with Python - Pandas, Seaborn, Matplotlib etc. after performing EDA learn how to apply ML algorithms on the datasets, create models and evaluate them.Supervised Learning:Understand how algorithms learn from labeled data to make predictions.Explore linear regression, logistic regression, decision trees, and more.Hands-on example: Predicting house prices, Titanic Survival prediction, etc..Unsupervised Learning:Learn to uncover hidden patterns in data without predefined labels.Topics include clustering.Hands-on example: Customer segmentation for marketing.Reinforcement Learning
iscover how agents learn to make decisions through rewards and penalties.Key concepts: Q-learning.Hands-on example.Key FeaturesBeginner-friendly, no ML knowledge required.Step by step tutorials on installing required IDEs and libraries.Step-by-step coding demonstrations in Python.Downloadable resources and cheat sheets for quick reference.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 What is Machine Learning - Theory
Lecture 3 Supervised Machine Learning - Classification - Theory
Lecture 4 Regression in Machine Learning - Theory and Maths
Lecture 5 What is Unsupervised Machine Learning - Theory
Lecture 6 Reinforcement Machine Learning - Theory
Section 2: IDE Installation and Usage
Lecture 7 How to install Python with System Environmental Variables for Data Science
Lecture 8 How Install Anaconda Navigator for Data Science and use it
Lecture 9 How to install Jupyter Notebook in computer for Data Analytics and Machine Learn
Lecture 10 How to use Google Colab for Data Analysis and Machine Learning with Python
Section 3: Exploratory Data Analysis with Example
Lecture 11 Exploratory Data Analysis on Titanic - Part 1 - Univariate Analysis
Lecture 12 Exploratory Data Analysis on Titanic - Part 2- Biivariate Analysis
Section 4: Regression Examples Using Python
Lecture 13 Linear Regression on One Variable Example
Lecture 14 Multiple Linear Regression Example
Lecture 15 What is Logistic Regression in Machine Learning - theory
Lecture 16 Logistic Regression on Study Hours vs Pass Fail Data
Section 5: Support Vector Machine Theory and Classification Example
Lecture 17 Support Vector Machine Algorithm - Theory
Lecture 18 Support Vector Machine Algorithm IRIS Data Classification using Python
Section 6: Random Forest Algorithm
Lecture 19 Random Forest Algorithm - Theory
Lecture 20 Random Forest Algorithm Example Using Python
Section 7: Decision Tree Algorithm
Lecture 21 Decision Tree Algorithm Theory
Lecture 22 Decision Tree Algorithm implementation with Python on Titanic Dataset
Section 8: Unsupervised Machine Learning - Example
Lecture 23 K Means Clustering Algorithm Part 1 - Theory ( Unsupervised)
Lecture 24 K means clustering algorithm example using Python - Part 2
Section 9: Q Learning Algorithm - Reinforcement Learning
Lecture 25 What is Q Learning Algorithm in Reinforcement Learning - Theory
Lecture 26 Q Learning Using Python Gym Module - Part 1
Lecture 27 Solving Problem Without Reinforcement Learning
Lecture 28 Applying Q Learning on Taxi V3 Environment - Using Python
Beginner Python Developers Curious about Machine Learning
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Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.00 GB | Duration: 8h 50m
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.00 GB | Duration: 8h 50m
Unlock the power of Machine Learning! Learn supervised, unsupervised and reinforcement learning with hands-on examples
What you'll learn
Supervised Machine Learning Algorithms and examples
Unsupervised Machine Learning Algorithms and examples
Reinforcement Algorithms and examples
Requirements
Basic understanding of Python Programming Language
Description
Are you curious about Machine Learning but have no prior experience? This course is perfect for you! Designed specifically for beginners, we break down the complexities of Machine Learning into simple, easy-to-understand concepts.Through real-world examples and practical exercises, you'll explore the foundations of supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, a professional looking to upskill, or simply a tech enthusiast, this course will provide you with the skills to kickstart your Machine Learning journey.Learn how to perform Exploratory Data Analysis with Python - Pandas, Seaborn, Matplotlib etc. after performing EDA learn how to apply ML algorithms on the datasets, create models and evaluate them.Supervised Learning:Understand how algorithms learn from labeled data to make predictions.Explore linear regression, logistic regression, decision trees, and more.Hands-on example: Predicting house prices, Titanic Survival prediction, etc..Unsupervised Learning:Learn to uncover hidden patterns in data without predefined labels.Topics include clustering.Hands-on example: Customer segmentation for marketing.Reinforcement Learning
iscover how agents learn to make decisions through rewards and penalties.Key concepts: Q-learning.Hands-on example.Key FeaturesBeginner-friendly, no ML knowledge required.Step by step tutorials on installing required IDEs and libraries.Step-by-step coding demonstrations in Python.Downloadable resources and cheat sheets for quick reference.Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 What is Machine Learning - Theory
Lecture 3 Supervised Machine Learning - Classification - Theory
Lecture 4 Regression in Machine Learning - Theory and Maths
Lecture 5 What is Unsupervised Machine Learning - Theory
Lecture 6 Reinforcement Machine Learning - Theory
Section 2: IDE Installation and Usage
Lecture 7 How to install Python with System Environmental Variables for Data Science
Lecture 8 How Install Anaconda Navigator for Data Science and use it
Lecture 9 How to install Jupyter Notebook in computer for Data Analytics and Machine Learn
Lecture 10 How to use Google Colab for Data Analysis and Machine Learning with Python
Section 3: Exploratory Data Analysis with Example
Lecture 11 Exploratory Data Analysis on Titanic - Part 1 - Univariate Analysis
Lecture 12 Exploratory Data Analysis on Titanic - Part 2- Biivariate Analysis
Section 4: Regression Examples Using Python
Lecture 13 Linear Regression on One Variable Example
Lecture 14 Multiple Linear Regression Example
Lecture 15 What is Logistic Regression in Machine Learning - theory
Lecture 16 Logistic Regression on Study Hours vs Pass Fail Data
Section 5: Support Vector Machine Theory and Classification Example
Lecture 17 Support Vector Machine Algorithm - Theory
Lecture 18 Support Vector Machine Algorithm IRIS Data Classification using Python
Section 6: Random Forest Algorithm
Lecture 19 Random Forest Algorithm - Theory
Lecture 20 Random Forest Algorithm Example Using Python
Section 7: Decision Tree Algorithm
Lecture 21 Decision Tree Algorithm Theory
Lecture 22 Decision Tree Algorithm implementation with Python on Titanic Dataset
Section 8: Unsupervised Machine Learning - Example
Lecture 23 K Means Clustering Algorithm Part 1 - Theory ( Unsupervised)
Lecture 24 K means clustering algorithm example using Python - Part 2
Section 9: Q Learning Algorithm - Reinforcement Learning
Lecture 25 What is Q Learning Algorithm in Reinforcement Learning - Theory
Lecture 26 Q Learning Using Python Gym Module - Part 1
Lecture 27 Solving Problem Without Reinforcement Learning
Lecture 28 Applying Q Learning on Taxi V3 Environment - Using Python
Beginner Python Developers Curious about Machine Learning
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