machine learning video demonstration tutorial (6 modules)
Budget: $30 – $250 USD
I'm looking for an experienced machine learning expert to create a tutorial series that covers the basics for those with beginner level knowledge. Each module should be no more than 15 minutes in length. While the tutorial should generally cover a range of machine learning concepts, I'm not specifically asking for any algorithm or concept to be emphasized in particular. If you feel there is something specific that could benefit beginners, please include it to your demonstration.
Each module 10 minutes (total 60mins, 6 module tutorial ) 720p and less than 4.0 GB. with video subtitle and instruction step of doing. cite resources also ( 2-3 links) , basic examples clarity is enough , no need to be complex
Lecture 1: Introduction to Machine Learning
Content: Overview of machine learning and key concepts like models, training data, supervised vs unsupervised learning. Explain real-world applications and tools like Python and Scikit-Learn.
Lecture 2: Regression Algorithms
Content: Implement linear regression and polynomial regression in Python. Explain regression evaluation metrics like RMSE. Tuning model parameters. Non-linear regression techniques.
Lecture 3: Classification Algorithms
Content: Implement logistic regression, decision trees, KNN for classification tasks. Evaluate using accuracy, precision, recall. Improve performance with techniques like regularization and ensembling.
Lecture 4: Clustering Algorithms
Content: Apply K-means and hierarchical clustering to find patterns in data. Evaluate cluster quality. Use principal component analysis for dimensionality reduction.
Lecture 5: Model Evaluation and Improvement
Content: Best practices for evaluating model performance using validation data. Improving models via parameter tuning, regularization, feature engineering. Overcoming common issues like overfitting.
Lecture 6: Applying Machine Learning to Real Data
Content: Guided projects analyzing real-world datasets end-to-end including data exploration, processing, modeling, and evaluating insights.
budget is $185 usd
Each module 10 minutes (total 60mins, 6 module tutorial ) 720p and less than 4.0 GB. with video subtitle and instruction step of doing. cite resources also ( 2-3 links) , basic examples clarity is enough , no need to be complex
Lecture 1: Introduction to Machine Learning
Content: Overview of machine learning and key concepts like models, training data, supervised vs unsupervised learning. Explain real-world applications and tools like Python and Scikit-Learn.
Lecture 2: Regression Algorithms
Content: Implement linear regression and polynomial regression in Python. Explain regression evaluation metrics like RMSE. Tuning model parameters. Non-linear regression techniques.
Lecture 3: Classification Algorithms
Content: Implement logistic regression, decision trees, KNN for classification tasks. Evaluate using accuracy, precision, recall. Improve performance with techniques like regularization and ensembling.
Lecture 4: Clustering Algorithms
Content: Apply K-means and hierarchical clustering to find patterns in data. Evaluate cluster quality. Use principal component analysis for dimensionality reduction.
Lecture 5: Model Evaluation and Improvement
Content: Best practices for evaluating model performance using validation data. Improving models via parameter tuning, regularization, feature engineering. Overcoming common issues like overfitting.
Lecture 6: Applying Machine Learning to Real Data
Content: Guided projects analyzing real-world datasets end-to-end including data exploration, processing, modeling, and evaluating insights.
budget is $185 usd