Simplified Machine Learning End To End™

With Case Study This comprehensive course offers an in-depth journey into Machine Learning and Data Science

What you'll learn

Introduction to Machine Learning:- Understand the basics and types of Machine Learning.

ML Unsupervised Learning:- Learn the concepts and techniques of Unsupervised Learning.

Supervised Learning - Regression:- Master regression models for predicting continuous outcomes.

Evaluation Metrics for Regression Model:- Evaluate regression models using metrics like MSE, RMSE, and R-squared.

Supervised Learning - Classification in Machine Learning:- Learn classification algorithms for categorical predictions.

Supervised Learning - Decision Trees:- Understand how Decision Trees work for classification and regression.

Unsupervised Learning - Clustering:- Explore clustering techniques to group data points.

Unsupervised Learning - DBSCAN Clustering: Apply the DBSCAN algorithm for density-based clustering.

Unsupervised Learning - Dimensionality Reduction:- Learn techniques to reduce data dimensions while retaining key information.

Unsupervised Learning - Dimensionality Reduction with t-SNE:- Use t-SNE for visualizing high-dimensional data in a reduced form.

Model Evaluation and Validation Techniques:- Understand model validation methods like cross-validation.

Model Evaluation - Bias-Variance Tradeoffs:- Learn to balance bias and variance for improved model performance.

Introduction to Python Libraries for Data Science:- Get familiar with key Python libraries such as NumPy, Pandas, and Scikit-learn.

Introduction to Python Libraries for Data Science:- Explore advanced Python libraries used in data analysis and machine learning.

Introduction to R Libraries for Data Science:- Learn essential R libraries for data manipulation and modeling.

Introduction to R Libraries for Data Science Statistical Modeling:- Apply statistical modeling using R's powerful libraries.

Requirements

Basic Understanding of Mathematics Familiarity with linear algebra, probability, and statistics is helpful.

Basic Analytical and Problem-Solving Skills Ability to think critically and solve complex problems.

Anyone can learn this class it is very simple.

Description

This comprehensive course offers an in-depth journey into Machine Learning and Data Science, designed to equip students with the skills needed to build and evaluate models, interpret data, and solve real-world problems. The course covers both Supervised and Unsupervised Learning techniques, with a strong focus on practical applications using Python and R.Students will explore essential topics like Regression, Classification, Clustering, and Dimensionality Reduction, alongside key model evaluation techniques, including the Bias-Variance Tradeoff and cross-validation. The course also includes an introduction to powerful libraries such as NumPy, Pandas, Scikit-learn, and t-SNE, along with statistical modeling in R.Whether you're a beginner or looking to enhance your knowledge in Machine Learning, this course provides the foundation and advanced insights necessary to master data science tools and methods, making it suitable for aspiring data scientists, analysts, or AI enthusiasts.Introduction to Machine Learning:- Understand the basics and types of Machine Learning.ML Unsupervised Learning:- Learn the concepts and techniques of Unsupervised Learning.Supervised Learning - Regression:- Master regression models for predicting continuous outcomes.Evaluation Metrics for Regression Model:- Evaluate regression models using metrics like MSE, RMSE, and R-squared.Supervised Learning - Classification in Machine Learning:- Learn classification algorithms for categorical predictions.Supervised Learning - Decision Trees:- Understand how Decision Trees work for classification and regression.Unsupervised Learning - Clustering:- Explore clustering techniques to group data points.Unsupervised Learning - DBSCAN Clustering: Apply the DBSCAN algorithm for density-based clustering.Unsupervised Learning - Dimensionality Reduction:- Learn techniques to reduce data dimensions while retaining key information.Unsupervised Learning - Dimensionality Reduction with t-SNE:- Use t-SNE for visualizing high-dimensional data in a reduced form.Model Evaluation and Validation Techniques:- Understand model validation methods like cross-validation.Model Evaluation - Bias-Variance Tradeoffs:- Learn to balance bias and variance for improved model performance.Introduction to Python Libraries for Data Science:- Get familiar with key Python libraries such as NumPy, Pandas, and Scikit-learn.Introduction to Python Libraries for Data Science:- Explore advanced Python libraries used in data analysis and machine learning.Introduction to R Libraries for Data Science:- Learn essential R libraries for data manipulation and modeling.Introduction to R Libraries for Data Science Statistical Modeling:- Apply statistical modeling using R's powerful libraries.Courtesy,Dr. FAK Noble Ai Researcher, Scientists, Product Developer, Innovator & Pure Consciousness ExpertFounder of Noble Transformation Hub TM

Overview

Section 1: Introduction to Machine Learning

Lecture 1 Introduction to Machine Learning

Section 2: ML Unsupervised Learning

Lecture 2 ML Unsupervised Learning

Section 3: Supervised Learning- Regression

Lecture 3 Supervised Learning- Regression

Section 4: Evaluation Metrics for Regression Model

Lecture 4 Evaluation Metrics for Regression Model

Section 5: Supervised Learning- Classification in Machine Learning

Lecture 5 Supervised Learning- Classification in Machine Learning

Section 6: Supervised Learning- Decision Trees

Lecture 6 Supervised Learning- Decision Trees

Section 7: Unsupervised Learning- Clustering

Lecture 7 Unsupervised Learning- Clustering

Section 8: Unsupervised Learning DBSCAN Clustering

Lecture 8 Unsupervised Learning DBSCAN Clustering

Section 9: Unsupervised Learning- Dimensionality Reduction

Lecture 9 Unsupervised Learning- Dimensionality Reduction

Section 10: Unsupervised Learning- Dimensionality Reduction with t-SNE

Lecture 10 Unsupervised Learning- Dimensionality Reduction with t-SNE

Section 11: Model Evaluation and Validation Techniques

Lecture 11 Model Evaluation and Validation Techniques

Section 12: Model Evaluation- Bias-Variance Tradeoffs

Lecture 12 Model Evaluation- Bias-Variance Tradeoffs

Section 13: Introduction to Python Libraries for Data Science

Lecture 13 Introduction to Python Libraries for Data Science

Section 14: Introduction to Python Libraries for Data Science

Lecture 14 Introduction to Python Libraries for Data Science

Section 15: Introduction to R Libraries for Data Science

Lecture 15 Introduction to R Libraries for Data Science

Section 16: Introduction to R Libraries for Data Science Statistical Modeling

Lecture 16 Introduction to R Libraries for Data Science Statistical Modeling

Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.