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Publicaciones del Canal
Foundations of Machine learning - Lecture Notes.pdf2.06 MB
| 2 |  Variance and Covariance
in Machine Learning | 1 031 |
| 3 | An Introduction to Statistics with Python
Thomas Haslwanter, 2016 | 1 219 |
| 4 | Machine Learning Handwritten Notes💡.pdf | 1 200 |
| 5 |  Roadmap To Master Machine Learning | 1 240 |
| 6 |  Top Machine Learning algorithms | 1 104 |
| 7 | What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy. | 1 105 |
| 8 | What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform. | 939 |
| 9 | What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability | 894 |
| 10 | +4 Python code for Artificial Intelligence: Foundations of Computational Agents | 1 059 |
| 11 | +3 Top 100 NLP Questions .pdf | 956 |
| 12 | TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers | 724 |
| 13 | +1 Programming Practice Python
Like for more | 714 |
| 14 | Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊 | 683 |
| 15 | Need more free resources related to? | 526 |
| 16 | +3 Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023 | 516 |
| 17 |  You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more...... | 462 |
| 18 | SQL for Data Science 📈.pdf | 494 |
| 19 | Machine Learning Interview Questions | 498 |
| 20 | Statistics Interview Q&A Part-2 | 510 |
