AI Journey 2024: Glimpse into AI-Driven Future

The AI Journey International Conference on Artificial Intelligence and Machine Learning will once again bring together developers, scientists, and AI enthusiasts. With 200+ speakers from more than ten countries, including China, India, UAE, Indonesia, and Iran, the conference will glimpse an AI-enriched future.

AI Journey will be held in Moscow on December 11–13, with each day highlighting a different track: Society, Business, and Science.

On December 11, the focus will be on Society, where BRICS experts, business, and government representatives will discuss the key role of technologies and AI as a means to address social issues. Attendees will gain insights into various AI-related success stories and how AI supports the sustainable development of the planet.

December 12 will be dedicated to Business. This track will feature leading experts such as Jaspreet Bindra, Dr. Aisha Bint Butti Bin Bishr, Janet Sawari, Karuna Gopal , and Hammam Riza, who will elaborate on real-world implementation of AI in business, and how business and industry can benefit from it.

December 13 will be all about Science. Sessions will feature international researchers sharing insights into the latest AI technology and the AI’s impact on research and science in general. Swagatam Das, Vladimir Spokoiny, Dedi Darwis, Gonzalo Ferrer, and other international experts will delve into the latest scientific advances ranging from generative models and quantum technologies to cybersecurity, educational tools, and medicine. Speakers from Sber, Moscow Institute of Physics and Technology, Innopolis University, and others will share how AI is transforming learning, development, reading, and art in everyday life. The Science Day will also immerse all AI newbies in the world of artificial intelligence with a special AIJ Junior track.

The AI Journey will host the awards ceremony for the finalists of the AI Challenge for young data scientists and the AIJ Contest for experienced AI professionals.

Join the live broadcast. Be up to date with the top AI news!

What are you learning?

𝗧𝗼𝗽 𝟴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

1. NumPy
→ Fundamental library for numerical computing.
→ Used for array operations, linear algebra, and random number generation.

2. Pandas
→ Best for data manipulation and analysis.
→ Offers DataFrame and Series structures for handling tabular data.

3. Matplotlib
→ Creates static, animated, and interactive visualizations.
→ Ideal for line charts, scatter plots, and bar graphs.

4. Seaborn
→ Built on Matplotlib for statistical data visualization.
→ Supports heatmaps, violin plots, and pair plots for deeper insights.

5. Scikit-Learn
→ Essential for machine learning tasks.
→ Provides tools for regression, classification, clustering, and preprocessing.

6. TensorFlow
→ Used for deep learning and neural networks.
→ Supports distributed computing for large-scale models.

7. SciPy
→ Extends NumPy with advanced scientific computations.
→ Useful for optimization, signal processing, and integration.

8. Statsmodels
→ Designed for statistical modeling and hypothesis testing.
→ Great for linear models, time series analysis, and statistical tests.

𝗧𝗶𝗽: Start with NumPy and Pandas to build your foundation, then explore others as per your data science needs!

Math for Data Science

GPT Promoting Frameworks

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.iss.one/datasciencefun

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Hope this helps you 😊

SQL Projects for Beginners to boost their resume!

1. Employee payroll management system
https://github.com/ojasphansekar/Employee-Payroll-Management-System

2. Library Management System
https://github.com/AlexanderWong/Library-Management-System

3. Student Database Management
https://github.com/shardul08/Student-DataBase-Management-System

4. SQL For Data Analysis Full Portfolio Project
https://youtube.com/watch?v=zZpMvAedh_E&ab_channel=WsCubeTech

5. Railway System Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/railway_system.html

More-> @dataportfolio

6. Inventory Control Management
https://vikingpathak.github.io/kh-sql-projects/markdown_files/inventory_control_management.html

7. Online Retail Application Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/online_retail_app.html

8. Employee-Payroll-Management-System
https://github.com/ojasphansekar/Employee-Payroll-Management-System

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Data Science With Python Workflow Cheat Sheet

Creator: business Science
Stars ⭐️: 75
Forked By: 38

https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf

Data Science Roadmap 2025

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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:

1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.

2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.

3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.

4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.

5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.

6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.

7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.

Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!

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ML Projects

Data Engineering Resources
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Skills Required in Data Analytics

🔟 Data Science Project Ideas for Freshers

Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.

Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.

Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.

Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.

Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.

Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).

Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.

Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.

Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.

A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.

Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.

Free datasets to build the projects
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ENJOY LEARNING 👍👍

Linear Algebra during exams: 'Not interested.'"
"Linear Algebra in AI/ML projects: 'Love at second sight!'"

Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.

Let's talk about it 🧵

There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization

The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.

Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.

This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.

In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).

More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.

Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.

https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing

Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.

https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk

Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.

Enable gradient descent to converge faster