Future Trends in Artificial Intelligence 👇👇

1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.

2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.

3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.

4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.

5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.

6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.

7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.

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Artificial Intelligence

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Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:

1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.

2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.

3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.

4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.

5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.

6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.

7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.

8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.

By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.

To start with Machine Learning:

   1. Learn Python
   2. Practice using Google Colab
   

Take these free courses:

https://t.iss.one/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:

https://t.iss.one/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.✌️✌️

Hi guys,

Join our main channel for all data science resources and free courses 👇👇
https://t.iss.one/datasciencefun

🔟 Free useful resources to learn Machine Learning

👉 Google

https://developers.google.com/machine-learning/crash-course

👉 Leetcode

https://leetcode.com/explore/featured/card/machine-learning-101

👉 Hackerrank

https://www.hackerrank.com/domains/ai/machine-learning

👉 Hands-on Machine Learning

https://t.iss.one/datasciencefun/424

👉 FreeCodeCamp

https://www.freecodecamp.org/learn/machine-learning-with-python/

👉 Machine learning projects

https://t.iss.one/datasciencefun/392

👉 Kaggle

https://www.kaggle.com/learn/intro-to-machine-learning

https://www.kaggle.com/learn/intermediate-machine-learning

👉 Geeksforgeeks

https://www.geeksforgeeks.org/machine-learning/

👉 Create ML Models

https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/

👉 Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍

For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

1. Supervised Learning:
    - Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
    - Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
    - Applications: Email spam detection, image recognition, and medical diagnosis.

2. Unsupervised Learning:
    - Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
    - Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
    - Applications: Customer segmentation, market basket analysis, and anomaly detection.

3. Reinforcement Learning:
    - Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
    - Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
    - Applications: Robotics, game playing (like AlphaGo), and self-driving cars.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content

ENJOY LEARNING 👍👍

To learn data structures and algorithms in Python, you can follow these steps:

1. Start with the basics: Learn about the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs. Learn how to implement them in Python and understand their time and space complexities.

2. Study algorithms: Study the most common algorithms for searching, sorting, and traversing data structures. Understand their time and space complexities and the trade-offs between different algorithms.

3. Practice, practice, practice: The more you practice implementing data structures and algorithms, the better you will get at it. You can start by solving problems on websites like LeetCode and HackerRank, or by working on small projects of your own.

4. Read and learn from others: Read articles and blogs written by experts in the field, and learn from their experiences and insights. Follow the work of other Python developers on Github, and see how they use data structures and algorithms in their projects.

How Google makes money

An important collection of the 15 best machine learning cheat sheets.

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

ENJOY LEARNING 👍👍

Excel & SQL Functions

The Untold Truth About Junior Data Analyst Interviews (From Someone Who’s Seen It All)

Guys, let’s cut through the noise. Most companies aren’t testing how many fancy tools you know—they’re testing how you think! Here’s what you really need to focus on:


SQL Interview Round

WHAT YOU THINK THEY WANT:
“Write the most complex SQL queries!”

WHAT THEY ACTUALLY TEST:

Can you clean messy data?

Do you handle NULL values logically?

How do you deal with duplicates?

Can you explain what you did, step-by-step?

Do you verify your results?


REALISTIC QUESTIONS YOU’LL FACE:
1️⃣ Find duplicate orders in a sales table.
2️⃣ Calculate monthly revenue for the past year.
3️⃣ Identify the top 10 customers by revenue.


Excel Interview Round

WHAT YOU THINK THEY WANT:
“Show off crazy Excel skills with macros and VBA.”

WHAT THEY REALLY WANT TO SEE:

Your ability to use VLOOKUP/XLOOKUP.

Comfort with Pivot Tables for summarization.

Your knack for creating basic formulas for data cleaning.

A logical approach to tackling Excel problems.


REALISTIC TASKS:
✅ Merge two datasets using VLOOKUP.
✅ Summarize sales trends in a Pivot Table.
✅ Clean up inconsistent text fields (hello, TRIM function).


Business Case Analysis

WHAT YOU THINK THEY WANT:
“Build a mind-blowing dashboard or deliver complex models.”

WHAT THEY ACTUALLY EVALUATE:

Can you break down the problem into manageable parts?

Do you ask smart, relevant questions?

Is your analysis focused on business outcomes?

How clearly can you present your findings?


What You'll Definitely Face

1. The “Data Mess” Scenario
They’ll hand you a messy dataset with:

Missing data, duplicates, and weird formats.

No clear instructions.


They watch:
👉 How you approach the problem.
👉 If you spot inconsistencies.
👉 The steps you take to clean and structure data.

2. The “Explain Your Analysis” Challenge
They’ll say:
“Walk us through what you did and why.”

They’re looking for:

Clarity in communication.

Your thought process.

The connection between your work and the business context.

How to Stand Out in Interviews

1. Nail the Basics

SQL: Focus on joins, filtering, grouping, and aggregating.

Excel: Get comfortable with lookups, pivots, and cleaning techniques.

Data Cleaning: Practice handling real-world messy datasets.


2. Understand the Business

Research their industry and common metrics (e.g., sales, churn rate).

Know basic KPIs they might ask about.

Prepare thoughtful, strategic questions.


3. Practice Real Scenarios
🔹 Analyze trends: Monthly revenue, churn analysis.
🔹 Segment customers: Who are your top spenders?
🔹 Evaluate campaigns: Which marketing effort drove the best ROI?


Reality Check: What Really Matters

🌟 How you think through a problem.
🌟 How you communicate your insights.
🌟 How you connect your work to business goals.

🚫 What doesn’t matter?

Writing overly complex SQL.

Knowing every Excel formula.

Advanced machine learning knowledge (for most junior roles).


Pro Tip: Stay calm, ask questions, and show you’re eager to solve problems. Your mindset is just as important as your technical skills!

Like this post if you want me to post more useful content ❤️

Hope it helps :)

21 essential skills

In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.

Here are some scenarios where using multiple scalers can be helpful in a data science project:

1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.

2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.

3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.

4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.

5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.

When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.

5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻

1. Dimensionality Reduction
- PCA, t-SNE, LDA

2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression

3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification

4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models

5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/free4unow_backup

Like if you need similar content 😄👍

Generative AI Mindmap
👇👇
https://t.iss.one/generativeai_gpt/164?single

Tensorflow vs Pytorch vs Keras