𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge👨‍💻📌

Thanks to edX, anyone can enroll in these free MIT-certified courses from anywhere in the world💻🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/43eM8I2

Let’s explore 5 of the best free courses MIT has to offer✅️

Use Chat GPT to prepare for your next Interview

This could be the most helpful thing for people aspiring for new jobs.

A few prompts that can help you here are:

💡Prompt 1: Here is a Job description of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}

💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}

💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?

💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?

💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?

Free book to master ChatGPT: https://t.iss.one/InterviewBooks/166

ENJOY LEARNING 👍👍

𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

Here’s your chance to build a solid foundation in artificial intelligence with the Oracle AI Foundations Associate course — absolutely FREE!💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3FfFOrC

No registration fee. No prior AI experience needed. Just pure learning to future-proof your career!✅️

𝟳+ 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

Here’s your golden chance to upskill with free, industry-recognized certifications from Google—all without spending a rupee!💰📌

These beginner-friendly courses cover everything from digital marketing to data tools like Google Ads, Analytics, and more⬇️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3H2YJX7

Tag them or share this post!✅️

Technical Questions Wipro may ask on their interviews

1. Data Structures and Algorithms:
   - "Can you explain the difference between an array and a linked list? When would you use one over the other in a real-world application?"
   - "Write code to implement a binary search algorithm."

2. Programming Languages:
   - "What is the difference between Java and C++? Can you provide an example of a situation where you would prefer one language over the other?"
   - "Write a program in your preferred programming language to reverse a string."

3. Database and SQL:
   - "Explain the ACID properties in the context of database transactions."
   - "Write an SQL query to retrieve all records from a 'customers' table where the 'country' column is 'India'."

4. Networking:
   - "What is the difference between TCP and UDP? When would you choose one over the other for a specific application?"
   - "Explain the concept of DNS (Domain Name System) and how it works."

5. System Design:
   - "Design a simple online messaging system. What components would you include, and how would they interact?"
   - "How would you ensure the scalability and fault tolerance of a web service or application?"

𝟲 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟 & 𝗠𝗟 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Looking to break into data analytics, data science, or machine learning this year?💻

These 6 free online courses from world-class universities and tech giants like Harvard, Stanford, MIT, Google, and IBM will help you build a job-ready skillset👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ksUTFi

Enjoy Learning ✅️

Tools Every AI Engineer Should Know

1. Data Science Tools
Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn.
R: Ideal for statistical analysis and data visualization.
Jupyter Notebook: Interactive coding environment for Python and R.
MATLAB: Used for mathematical modeling and algorithm development.
RapidMiner: Drag-and-drop platform for machine learning workflows.
KNIME: Open-source analytics platform for data integration and analysis.

2. Machine Learning Tools
Scikit-learn: Comprehensive library for traditional ML algorithms.
XGBoost & LightGBM: Specialized tools for gradient boosting.
TensorFlow: Open-source framework for ML and DL.
PyTorch: Popular DL framework with a dynamic computation graph.
H2O.ai: Scalable platform for ML and AutoML.
Auto-sklearn: AutoML for automating the ML pipeline.

3. Deep Learning Tools
Keras: User-friendly high-level API for building neural networks.
PyTorch: Excellent for research and production in DL.
TensorFlow: Versatile for both research and deployment.
ONNX: Open format for model interoperability.
OpenCV: For image processing and computer vision.
Hugging Face: Focused on natural language processing.

4. Data Engineering Tools
Apache Hadoop: Framework for distributed storage and processing.
Apache Spark: Fast cluster-computing framework.
Kafka: Distributed streaming platform.
Airflow: Workflow automation tool.
Fivetran: ETL tool for data integration.
dbt: Data transformation tool using SQL.

5. Data Visualization Tools
Tableau: Drag-and-drop BI tool for interactive dashboards.
Power BI: Microsoft’s BI platform for data analysis and visualization.
Matplotlib & Seaborn: Python libraries for static and interactive plots.
Plotly: Interactive plotting library with Dash for web apps.
D3.js: JavaScript library for creating dynamic web visualizations.

6. Cloud Platforms
AWS: Services like SageMaker for ML model building.
Google Cloud Platform (GCP): Tools like BigQuery and AutoML.
Microsoft Azure: Azure ML Studio for ML workflows.
IBM Watson: AI platform for custom model development.

7. Version Control and Collaboration Tools
Git: Version control system.
GitHub/GitLab: Platforms for code sharing and collaboration.
Bitbucket: Version control for teams.

8. Other Essential Tools
Docker: For containerizing applications.
Kubernetes: Orchestration of containerized applications.
MLflow: Experiment tracking and deployment.
Weights & Biases (W&B): Experiment tracking and collaboration.
Pandas Profiling: Automated data profiling.
BigQuery/Athena: Serverless data warehousing tools.
Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

#artificialintelligence

𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗔𝗱𝗱 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Looking to land an internship, secure a tech job, or start freelancing in 2025?👨‍💻

Python projects are one of the best ways to showcase your skills and stand out in today’s competitive job market🗣📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4kvrfiL

Stand out in today’s competitive job market✅️

𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!)😍

Start Here — With Zero Cost and Maximum Value!💰📌

If you’re aiming for a career in data analytics, now is the perfect time to get started🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Fq7E4p

A great starting point if you’re brand new to the field✅️

100 Days Data Science Challenge 👆

𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨‍🎓📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3GZZUXi

All at zero cost!🎊✅️

𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Ready to upskill in data science for free?🚀

Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/43GspSO

Take the first step towards your dream career!✅️

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.

𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨‍🎓📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3GZZUXi

All at zero cost!🎊✅️

3 ways to keep your data science skills up-to-date

1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.

2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.

3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲𝘀 – 𝗙𝗿𝗲𝗲 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗚𝘂𝗶𝗱𝗲😍

If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jg88I8

All The Best 🎊