Simplified Machine Learning End To End™
Simplified Machine Learning End To End™
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Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.01 GB | Duration: 7h 15m
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.
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