Machine Learning & Artificial Intelligence | Data Science Free Courses
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شبکه:📈 تحلیل کانال تلگرام Machine Learning & Artificial Intelligence | Data Science Free Courses
کانال Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 68 668 مشترک است و جایگاه 2 389 را در دسته آموزش و رتبه 411 را در منطقه ماليزيا دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 68 668 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 30 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 230 و در ۲۴ ساعت گذشته برابر 3 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.42% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.81% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 351 بازدید دریافت می‌کند. در اولین روز معمولاً 557 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 4 است.
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“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 31 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.
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آرشیو پست ها
Why is it require to split our data into three parts: train, validation, and test?
• The training set is used to fit the model, i.e. to train the model with the data.
• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
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Metaheuristic Algorithms.pdf21.46 MB
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Fundamentals of Data Science.pdf12.36 MB
Data Science from Scratch First Principles with Python (Joel Grus)
ML+Cheat+Sheet_2.pdf3.31 MB
Machine Learning and AI Foundations: Causal Inference and Modeling
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Top 10 essential data science terminologies
1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data.
2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from.
3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis.
4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language.
6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data.
7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models.
8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily.
9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting.
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Understanding Bias and Variance in Machine Learning
Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).
Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)
There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting.
Techniques like cross validation can be helpful in these cases.
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Popular Python packages for data science:
1. NumPy: For numerical operations and working with arrays.
2. Pandas: For data manipulation and analysis, especially with data frames.
3. Matplotlib and Seaborn: For data visualization.
4. Scikit-learn: For machine learning algorithms and tools.
5. TensorFlow and PyTorch: Deep learning frameworks.
6. SciPy: For scientific and technical computing.
7. Statsmodels: For statistical modeling and hypothesis testing.
8. NLTK and SpaCy: Natural Language Processing libraries.
9. Jupyter Notebooks: Interactive computing and data visualization.
10. Bokeh and Plotly: Additional libraries for interactive visualizations.
