Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
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Network:π Analytical overview of Telegram channel Machine Learning & Artificial Intelligence | Data Science Free Courses
Channel Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) in the English language segment is an active participant. Currently, the community unites 68 672 subscribers, ranking 2 392 in the Education category and 410 in the Malaysia region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 672 subscribers.
According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 188 over the last 30 days and by -24 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.42%. Within the first 24 hours after publication, content typically collects 0.81% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 660 views. Within the first day, a publication typically gains 557 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as sellerflash, waybienad, pricing, buybox, buyer.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βPerfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfunβ
Thanks to the high frequency of updates (latest data received on 01 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
68 672
Subscribers
-2424 hours
+117 days
+18830 days
Posts Archive
Why is it require to split our data into three parts: train, validation, and test?
β’ The training set is used to fit the model, i.e. to train the model with the data.
β’ 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.
β’ 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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If you are passionate about creating your own startup or business in future, then you can refer to this channel for amazing ideas and inspiration.
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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.
Credits: https://t.me/datasciencefree
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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.
