Data Science & Machine Learning
16 Mar 2024 17:30
ATTENTION!!
+1000% coin will be posted in BINANCE WHALE'S LEAK🚀🚀
Link open only for LIMITED TIME🕓
JOIN FAST👀👇
/channel/+rDT7H_njmis4ODQ0
Читать полностью…
Data Science & Machine Learning
15 Mar 2024 13:58
Statistical Tests in AB Testing
Читать полностью…
Data Science & Machine Learning
11 Mar 2024 14:55
Best Telegram channels to get free coding & data science resources
👇👇
/channel/addlist/ID95piZJZa0wYzk5
Machine Learning & AI Resources
👇👇
/channel/machinelearning_deeplearning
Читать полностью…
Data Science & Machine Learning
01 Mar 2024 18:05
Learning data science in 2024 will likely involve a combination of traditional educational methods and newer, more innovative approaches.
Here are some steps you can take to learn data science in 2024:
1. Enroll in a data science program: Consider enrolling in a data science program at a university or online platform. Look for programs that cover topics such as machine learning, statistical analysis, and data visualization. I will recommend the subscription by 365datascience which update content as per latest requirements.
2. Take online courses: There are many online platforms that offer data science courses, such as Udacity, Udemy, and DataCamp. These courses can help you learn specific skills and techniques in data science.
3. Participate in data science competitions: Participating in data science competitions, such as those hosted on Kaggle, can help you apply your skills to real-world problems and learn from other data scientists.
4. Join data science communities: Joining data science communities, such as forums, meetups, or social media groups, can help you connect with other data scientists and learn from their experiences.
5. Stay updated on industry trends: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow blogs, podcasts, and industry publications to keep up with the latest developments in data science.
6. Build a portfolio: As you learn data science skills, be sure to build a portfolio of projects that showcase your abilities. This can help you demonstrate your skills to potential employers or clients.
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
10 Feb 2024 16:57
Python libraries for data science and Machine Learning 👇👇
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation and analysis library that provides data structures like DataFrames and Series, making it easy to work with structured data.
3. Matplotlib: Matplotlib is a plotting library that enables the creation of various types of visualizations, such as line plots, bar charts, histograms, scatter plots, etc., to explore and communicate data effectively.
4. Scikit-learn: Scikit-learn is a machine learning library that offers a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also provides tools for model selection and evaluation.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools and libraries for developing and deploying machine learning applications.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
7. SciPy: SciPy is a scientific computing library that builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, signal processing, and more.
8. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a higher-level interface for creating attractive and informative statistical graphics.
Channel credits: /channel/datasciencefun
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
08 Feb 2024 19:51
Pandas is a popular Python library for data manipulation and analysis. Here are some essential concepts in Pandas that every data analyst should be familiar with:
1. Data Structures: Pandas provides two main data structures: Series and DataFrame. A Series is a one-dimensional array-like object, while a DataFrame is a two-dimensional tabular data structure similar to a spreadsheet.
2. Indexing and Selection: Pandas allows you to select and manipulate data using various indexing techniques, such as label-based indexing (loc), integer-based indexing (iloc), and boolean indexing.
3. Data Cleaning: Pandas provides functions for handling missing data, removing duplicates, and filling in missing values. Methods like dropna(), fillna(), and drop_duplicates() are commonly used for data cleaning.
4. Data Manipulation: Pandas offers powerful tools for data manipulation, such as merging, joining, concatenating, reshaping, and grouping data. Functions like merge(), concat(), pivot_table(), and groupby() are commonly used for data manipulation tasks.
5. Data Aggregation: Pandas allows you to aggregate data using functions like sum(), mean(), count(), min(), max(), and custom aggregation functions. These functions help summarize and analyze data at different levels.
6. Time Series Analysis: Pandas has built-in support for working with time series data, including date/time indexing, resampling, shifting, rolling window calculations, and time zone handling.
7. Data Visualization: Pandas integrates well with popular data visualization libraries like Matplotlib and Seaborn to create visualizations directly from DataFrames. You can plot data using functions like plot(), hist(), scatter(), and boxplot().
8. Handling Categorical Data: Pandas provides support for working with categorical data through the Categorical data type. This helps in efficient storage and analysis of categorical variables.
9. Reading and Writing Data: Pandas can read data from various file formats such as CSV, Excel, SQL databases, JSON, and HTML. It can also write data back to these formats after processing.
10. Performance Optimization: Pandas offers methods to optimize performance, such as vectorized operations (using NumPy arrays), using apply() function efficiently, and avoiding loops for faster data processing.
By mastering these essential concepts in Pandas, you can efficiently manipulate and analyze data, perform complex operations, and derive valuable insights from your datasets as a data analyst. Regular practice and hands-on experience with Pandas will further enhance your skills in data manipulation and analysis.
Читать полностью…
Data Science & Machine Learning
05 Feb 2024 14:41
Amazing response guys!
Let's start with the first algorithm:
1. Linear Regression:
- Parameter:
- None (for basic linear regression): There are no specific hyperparameters for a simple linear regression model.
- Why: Linear regression is a straightforward algorithm where the model fits a line to the data, and there are minimal parameters to tweak. The primary focus is often on the quality of the data and assumptions related to linearity.
Читать полностью…
Data Science & Machine Learning
04 Feb 2024 06:15
Thanks for the amazing response in last post
Here is a simple explanation of each algorithm:
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: /channel/datasciencefun
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
07 Jan 2024 06:22
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
/channel/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
/channel/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
Читать полностью…
Data Science & Machine Learning
16 Mar 2024 17:30
Paid Ad
(Please don't give your money to them)
Читать полностью…
Data Science & Machine Learning
12 Mar 2024 20:25
Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
03 Mar 2024 05:50
🚦Top 10 Data Science Tools🚦
Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.
🛰What is Data Science ?
Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .
🗽Top Data Science Tools that are normally utilized :
1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .
2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.
3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.
4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.
5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.
6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.
7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.
8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.
9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.
10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.
Читать полностью…
Data Science & Machine Learning
27 Feb 2024 18:12
Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: /channel/datasciencefun
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
19 Feb 2024 13:58
logistic regression notes.pdf
Читать полностью…
Data Science & Machine Learning
10 Feb 2024 05:25
Essential Data Science Concepts 👇
1. Data cleaning: The process of identifying and correcting errors or inconsistencies in data to improve its quality and accuracy.
2. Data exploration: The initial analysis of data to understand its structure, patterns, and relationships.
3. Descriptive statistics: Methods for summarizing and describing the main features of a dataset, such as mean, median, mode, variance, and standard deviation.
4. Inferential statistics: Techniques for making predictions or inferences about a population based on a sample of data.
5. Hypothesis testing: A method for determining whether a hypothesis about a population is true or false based on sample data.
6. Machine learning: A subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.
7. Supervised learning: A type of machine learning where the model is trained on labeled data to make predictions on new, unseen data.
8. Unsupervised learning: A type of machine learning where the model is trained on unlabeled data to find patterns or relationships within the data.
9. Feature engineering: The process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models.
10. Model evaluation: The process of assessing the performance of a machine learning model using metrics such as accuracy, precision, recall, and F1 score.
Читать полностью…
Data Science & Machine Learning
06 Feb 2024 15:43
2. Decision Trees:
- Parameters:
- Max Depth: Limits the depth of the tree by restricting the number of questions it can ask.
- Min Samples Split: Specifies the minimum number of samples required to split a node.
- Min Samples Leaf: Sets the minimum number of samples a leaf node must have.
- Why: These parameters control the complexity of the decision tree. Adjusting them helps prevent overfitting (capturing noise in the data) and ensures a more generalizable model.
Читать полностью…
Data Science & Machine Learning
04 Feb 2024 08:19
Deep from Kaggle Group asked me to explain each parameters used in ml algorithms and why we use it in detail.
Like this post if you want next few posts on that topic
Читать полностью…
Data Science & Machine Learning
03 Feb 2024 11:16
Important Machine Learning Algorithms 👇👇
- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)
Like this post if you want me to explain each algorithm in detail
Share with credits: /channel/datasciencefun
ENJOY LEARNING 👍👍
Читать полностью…
Data Science & Machine Learning
04 Jan 2024 19:37
Machine Learning for Decision Makers
👇👇
/channel/machinelearning_deeplearning/110
Читать полностью…
Data Science & Machine Learning
27 Dec 2023 07:55
Python Real-world Projects
👇👇
/channel/pythonspecialist/105
Читать полностью…
50007