Heather Dawe’s Post

Thank you Matt Eland for a concise review of mine and Heather Dawe's book ‘Responsible AI in the Enterprise’. In light of the increasing clarity in AI regulation worldwide and the growing emphasis on AI risk assessment and governance within enterprises, the themes explored in our book are proving to be more pertinent than ever. "Responsible AI in the Enterprise by Adnan Masood, PhD.. and Heather Dawe was a great treatment of all the things that can go wrong in AI / ML projects, important principles in the design and operation of these systems, and ways of detecting and mitigating fairness issues in AI Systems." You can find the book here. https://lnkd.in/eNiPGQDw #ResponsibleAI #AIEthics #AI #MVPBuzz #RDBuzz

I have some cool things to share in the near future on libraries I've been developing and a few new projects on the horizon, but for a moment I want to highlight some learning efforts from my trip out to Nevada a few weeks ago. Whenever I fly, I read a book or two on each plane flight using a large print eReader. I read a number of books on my 3 flights including: C# Interview Guide by Konstantin Semenenko 🇺🇦 - an interesting exploration of .NET through a wide spectrum of interview questions. While no answer was comprehensive, the book as a whole took a very wide look at .NET development and would give the reader a number of things to think about and look into. I personally learned of a few new libraries to dig into and thought about a few language capabilities in different ways. Plus, the book surprised me by mentioning my book, Refactoring with C#, which made me randomly laugh in bemusement aboard the plane. C# Interview Guide is definitely one I'd recommend to folks looking to establish a foothold as a .NET developer or find areas to get deeper in. Python Feature Engineering Cookbook by Soledad Galli. This was a fun exploration of the myriad ways you can modify and introduce features to datasets in Python and the common problems that might cause you to consider doing so. The book focused a lot on unusual distributions, outliers, and other common tasks a data scientist or data analyst must perform and was a great educational resource. Responsible AI in the Enterprise by Adnan Masood, PhD. and Heather Dawe was a great treatment of all the things that can go wrong in AI / ML projects, important principles in the design and operation of these systems, and ways of detecting and mitigating fairness issues in AI Systems. This last part was a key factor in purchasing the book for me as I wanted a more in-depth exploration of Fairlearn and enjoyed the book's treatment of it. Hands-on Data Processing in Python by Roy Jafari. I didn't quite finish this one before I got back home, but this is a great treatment of different ways you might need to wrangle your data and gain control over it. It surprised me by setting a good foundation on machine learning in its opening 8 chapters before digging deep into progressively cleaning data, transforming it, and reducing its dimensionality. The book looks to end with a series of case studies, and I'm looking forward to finishing the remaining 35 pages or so in the near future. All in all, it was a great set of books to get me thinking deeper about data and its handling, help me wrap my head around some of the nuances of data distributions, and give me new things to look into in more detail and experiment with.

Python Methods Explained with a Creative Twist! 🍟 🔍 Looking for a unique way to learn Python methods? This visual guide breaks down essential list operations with fun food icons! Here's a sneak peek: ✨ .append() – Add a new item to the end of your list. ✨ .clear() – Remove all items from your list. ✨ .count() – Find how many times an item appears. ✨ .index() – Get the position of an item. ✨ .pop() – Remove an item at a specified position. ✨ .reverse() – Flip the list order. Learning Python has never been this engaging! 🍔🍟🧋 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗿𝗲𝗴𝗿𝗲𝘁 𝗻𝗼𝘁 𝘁𝗮𝗸𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟰. 1 Introduction Generative to Al 🔗 https://lnkd.in/detitq8h 2. Generative AI with Large Language Models 🔗 https://lnkd.in/dKkYeknp 3. Generative Adversarial Networks (GANs) Specialization 🔗 https://lnkd.in/dyXgBExM 4. Introduction to Artificial Intelligence (AI) 🔗 https://lnkd.in/d2Awst5W 5. Generative AI Primer 🔗 https://lnkd.in/d5Mxw9m3 6. Natural Language Processing Specialization 🔗 https://lnkd.in/dwHpf5St 7. Deep Learning Specialization 🔗 https://lnkd.in/d6cWvJ_9 8. Generative AI for Data Scientists Specialization 🔗 https://lnkd.in/d-MCw8k5 9. IBM Data Science Professional Certificate 🔗 https://lnkd.in/d2cBszG5 10. Introduction to Data Science 🔗 https://lnkd.in/dKGf-KtB 11. Learn SQL Basics for Data Science 🔗 https://lnkd.in/dCpZ-NRX 12. Python for Everybody 🔗 https://lnkd.in/dicyAtC4 13. Machine Learning Specialization 🔗 https://lnkd.in/dW7wUUcx 14. Data Science Fundamentals with Python & SQL Specialization 🔗 https://lnkd.in/dkfRpT9e 15. Excel Skills for Data Analytics and Visualization 🔗 https://lnkd.in/dgvEw2e5 16. Crash Course on Python 🔗 https://lnkd.in/dsCQJQpk 17. IBM Data Analytics with Excel and R 🔗https://lnkd.in/duHEEBRR 18. Excel to MySQL: Analytic Techniques for Business 🔗 https://lnkd.in/dfpewZ-b 19. Advanced Google Analytics 🔗https://lnkd.in/d-n-za6p 20. Google Project Management 🔗https://lnkd.in/dtTiGX8N 21. Agile Project Management 🔗https://lnkd.in/d_Zk7zdi 22. Project Execution: Running the Project 🔗https://lnkd.in/d69b7erj 23. Foundations of Project Management 🔗https://lnkd.in/dy77uH67 24. Project Initiation: Starting a Successful Project 🔗https://lnkd.in/dsZFaNmi 25. Project Planning: Putting It All Together 🔗https://lnkd.in/d5zrVak6 26. Google Data Analytics: 🔗 https://lnkd.in/dVAzUSJd 27. Get Started with Python 🔗 https://lnkd.in/diX9mRw6 28. Learn Python Basics for Data Analysis 🔗https://lnkd.in/dimjFgx5 https://lnkd.in/dz2AZZB8 29. Google Advanced Data Analytics Capstone 🔗 https://lnkd.in/dcVTcbih 30. Data Analysis with R Programming 🔗 https://lnkd.in/dwpP4xT3 follow Rushika Rai for more #onlinelearning #google #coursera #ai

𝗨𝗻𝘃𝗲𝗶𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲: 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗟 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Python is more than just a programming language - it's your gateway to the exciting world of Artificial Intelligence (AI) and Machine Learning (ML)! Whether you're a curious beginner or a tech enthusiast, Python's clear syntax and powerful libraries make it the perfect choice to kickstart your AI/ML journey. Here's why: 𝐄𝐚𝐬𝐲 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 : Python reads almost like plain English, making it a breeze to pick up even if you have no coding experience. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐟𝐨𝐫 𝐀𝐈/𝐌𝐋 : Libraries like NumPy and Pandas provide the tools you need to manipulate data, build models, and unlock incredible AI potential. 𝐇𝐮𝐠𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Millions of developers use Python, offering tons of resources, tutorials, and support to help you on your way. 𝟏. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬: 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 & 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬 : Master storing and manipulating numbers (integers, floats), text (strings), and true/false values (Booleans). 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 & 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐅𝐥𝐨𝐰 : Use operators (+, -, *, /) to perform calculations and control how your code executes with if-else statements and loops (for, while). 𝟐. 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 : 𝐋𝐢𝐬𝐭𝐬 : Organize and access ordered collections of items, like training data for your AI model. 𝐓𝐮𝐩𝐥𝐞𝐬 : Similar to lists, but immutable (can't be changed), useful for storing constant data. 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬 : Efficiently store and retrieve data using key-value pairs, perfect for mapping features to labels in an ML dataset. 𝟑. 𝐍𝐮𝐦𝐏𝐲: 𝐓𝐡𝐞 𝐌𝐚𝐭𝐡 𝐖𝐢𝐳 Arrays: Unleash the power of multidimensional data structures for numerical computations in AI algorithms (e.g., linear regression). Vectorized Operations: Perform calculations on entire arrays at once, speeding up data processing for your ML models. 𝟒. 𝐏𝐚𝐧𝐝𝐚𝐬: 𝐃𝐚𝐭𝐚 𝐖𝐫𝐚𝐧𝐠𝐥𝐢𝐧𝐠 𝐇𝐞𝐫𝐨 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞𝐬 : Master tabular data with labeled axes, making it easy to work with large datasets. 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 : Prepare your data efficiently for AI/ML by removing errors, handling missing values, and formatting. 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 : Slice and dice your data to uncover patterns and insights for smarter model training. 𝟓. 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛: 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐬𝐭𝐞𝐫 𝐋𝐢𝐧𝐞 𝐏𝐥𝐨𝐭𝐬, 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭𝐬, 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭𝐬 : Create clear and informative visualizations to understand your data and the results of your AI/ML models. Ready to embark on your Python-powered AI/ML journey? Check out the comments for awesome learning resources! #pakdatascience #datascience #machinelearning #Python #languagelearning #artificialintelligence #xevensolutions #codanics Shaheryar Yousaf Mehran Ali Mubashir Ahmad Muhammad Haris Tariq Dr. Sheraz Naseer - (PhD Artificial Intelligence, Data Science)

𝗨𝗻𝘃𝗲𝗶𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲: 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗟 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Python is more than just a programming language - it's your gateway to the exciting world of Artificial Intelligence (AI) and Machine Learning (ML)! Whether you're a curious beginner or a tech enthusiast, Python's clear syntax and powerful libraries make it the perfect choice to kickstart your AI/ML journey. Here's why: 𝐄𝐚𝐬𝐲 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 : Python reads almost like plain English, making it a breeze to pick up even if you have no coding experience. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐟𝐨𝐫 𝐀𝐈/𝐌𝐋 : Libraries like NumPy and Pandas provide the tools you need to manipulate data, build models, and unlock incredible AI potential. 𝐇𝐮𝐠𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Millions of developers use Python, offering tons of resources, tutorials, and support to help you on your way. 𝟏. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬: 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 & 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬 : Master storing and manipulating numbers (integers, floats), text (strings), and true/false values (Booleans). 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 & 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐅𝐥𝐨𝐰 : Use operators (+, -, *, /) to perform calculations and control how your code executes with if-else statements and loops (for, while). 𝟐. 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 : 𝐋𝐢𝐬𝐭𝐬 : Organize and access ordered collections of items, like training data for your AI model. 𝐓𝐮𝐩𝐥𝐞𝐬 : Similar to lists, but immutable (can't be changed), useful for storing constant data. 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬 : Efficiently store and retrieve data using key-value pairs, perfect for mapping features to labels in an ML dataset. 𝟑. 𝐍𝐮𝐦𝐏𝐲: 𝐓𝐡𝐞 𝐌𝐚𝐭𝐡 𝐖𝐢𝐳 Arrays: Unleash the power of multidimensional data structures for numerical computations in AI algorithms (e.g., linear regression). Vectorized Operations: Perform calculations on entire arrays at once, speeding up data processing for your ML models. 𝟒. 𝐏𝐚𝐧𝐝𝐚𝐬: 𝐃𝐚𝐭𝐚 𝐖𝐫𝐚𝐧𝐠𝐥𝐢𝐧𝐠 𝐇𝐞𝐫𝐨 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞𝐬 : Master tabular data with labeled axes, making it easy to work with large datasets. 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 : Prepare your data efficiently for AI/ML by removing errors, handling missing values, and formatting. 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 : Slice and dice your data to uncover patterns and insights for smarter model training. 𝟓. 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛: 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐬𝐭𝐞𝐫 𝐋𝐢𝐧𝐞 𝐏𝐥𝐨𝐭𝐬, 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭𝐬, 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭𝐬 : Create clear and informative visualizations to understand your data and the results of your AI/ML models. Ready to embark on your Python-powered AI/ML journey? Check out the comments for awesome learning resources!

Python libraries that are very useful but lesser known!! Here are some useful but less popular Python libraries along with their applications: 1. NetworkX: library for creating, analyzing, and visualizing complex networks or graphs. It's used in various domains such as social network analysis, biological network analysis, transportation network analysis, and more. 2.Beautiful Soup: library for web scraping that makes it easy to extract information from HTML and XML files. It's commonly used for data extraction and web scraping tasks in web development, data mining, and research projects. 3.Plotly: Python graphing library that produces interactive, publication-quality graphs. It supports various chart types, including scatter plots, line plots, bar charts, and heatmaps. Plotly is useful for creating interactive visualizations for data analysis, dashboard development, and presentations. 4.Gensim: Gensim is a library for topic modeling and natural language processing (NLP). It provides algorithms and tools for semantic analysis, document similarity calculation, text summarization, and topic modeling using techniques like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). 5.Folium: Folium is a Python library for creating interactive leaflet maps using data from Pandas DataFrames. It's built on top of the Leaflet JavaScript library and allows users to create maps with markers, polygons, heatmaps, and other customizations. Folium is commonly used for visualizing geospatial data and creating interactive web maps. 6.SymPy: SymPy is a Python library for symbolic mathematics. It provides tools for symbolic computation, algebraic manipulation, calculus, equation solving, and more. SymPy is useful for symbolic mathematics research, education, and engineering applications where exact mathematical expressions are required. 7.SQLAlchemy: SQLAlchemy is a SQL toolkit and Object-Relational Mapping (ORM) library for Python. It provides a high-level interface for interacting with relational databases using Python objects. SQLAlchemy simplifies database operations such as querying, inserting, updating, and deleting data and is commonly used in web development, data analysis, and database-driven applications. there are more such libraries. share in the comments if you know any such libraries!! #python3 #pythondevelopment

𝗪𝗵𝗮𝘁 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜  🚀 Python has solidified its role as the go-to language for AI. Here’s how you can tap into its power across AI fields: 1. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴  🧠 Frameworks like TensorFlow and PyTorch simplify training models for tasks like visual recognition and fraud detection. Tip: Start with pre-trained models or build your own for industry-specific solutions. 2. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🌊 Build powerful neural networks with Python libraries like PyTorch and Keras. Tip: Look into SGD and Adam optimization algorithms for training efficiency. 3. 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗡𝗟𝗣) 🗣️ Python makes it easy to add language understanding to apps, thanks to libraries like NLTK, SpaCy, and Transformers by Hugging Face. Tip: Use these libraries to create conversational bots or apps with advanced text analysis. 4. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀🔮 Tools like scikit-learn and data libraries (pandas, numpy, matplotlib) help turn historical data into actionable insights. Tip: Try Jupyter Notebooks for real-time analysis or build apps for end users in specific industries like healthcare. 5. 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝘁𝗼 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 💡 Want to dive deeper? Contribute to open-source AI libraries like PyTorch on GitHub. This boosts your knowledge of AI fundamentals and helps you give back to the community. Mastering AI with Python opens up new possibilities, from building smarter apps to making contributions to the very frameworks you use. Ready to unlock Python’s full AI potential? 𝗢𝘂𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: 𝗦𝘁𝗮𝗳𝗳𝗶𝗻𝗴: We offer contract, contract to hire, direct hire, remote global hiring, SOW projects and managed services. https://lnkd.in/g6bddCHa 𝗥𝗲𝗺𝗼𝘁𝗲 𝗛𝗶𝗿𝗶𝗻𝗴: We offer U.S. companies the opportunity to hire IT professionals from our India-based talent network. https://lnkd.in/gN2A4c-Y 𝗖𝘂𝘀𝘁𝗼𝗺 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: We offer Web/Mobile Development, UI/UX Design, QA & Automation, API Integration, DevOps services and Product Development. https://lnkd.in/dcKsvxAu 𝗢𝘂𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 𝗭𝗲𝗻𝗕𝗮𝘀𝗸𝗲𝘁 :An E-commerce platform to sell your products online to a large user base with custom features. https://getzenbasket.com/ 𝗭𝗲𝗻𝘆𝗼 𝗣𝗮𝘆𝗿𝗼𝗹𝗹: An automated payroll application that helps companies in India process their employees' payroll. https://lnkd.in/gvDg-Uds 𝗭𝗲𝗻𝘆𝗼 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲: Simplifying all HR processes and maximizing productivity by automating routine tasks. https://lnkd.in/grcEACXM #PythonForAI #MachineLearning #DeepLearning #NLP #PredictiveAnalytics #OpenSource #AIFrameworks #DataScience #ArtificialIntelligence #TechInnovation #Centizen #ZenBasket

𝐃𝐀𝐓𝐀 𝐒𝐂𝐈𝐄𝐍𝐂𝐄 𝐑𝐎𝐀𝐃𝐌𝐀𝐏 𝐌𝐨𝐧𝐭𝐡 1: 𝐁𝐚𝐬𝐢𝐜 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Learn the fundamentals of Python programming. This includes understanding data types, variables, loops, conditional statements, and basic functions. 𝐌𝐨𝐧𝐭𝐡 2: 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐅𝐨𝐜𝐮𝐬: Study essential statistical concepts and probability theory. This forms the foundation for data analysis and understanding data distributions. 𝐌𝐨𝐧𝐭𝐡 3: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Enhance your Python skills by diving into advanced topics such as object-oriented programming, decorators, generators, and modules. 𝐌𝐨𝐧𝐭𝐡 4: 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Learn how to visualize data using libraries such as Matplotlib, Seaborn, and Plotly. Understanding how to create informative and compelling visualizations is key to data communication. 𝐌𝐨𝐧𝐭𝐡 5: 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐨𝐜𝐮𝐬: Start learning the basics of machine learning. This includes understanding algorithms like linear regression, decision trees, and clustering techniques. Libraries such as Scikit-Learn will be crucial. 𝐌𝐨𝐧𝐭𝐡 6: 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Develop skills in data manipulation and cleaning using libraries such as Pandas and NumPy. This step is vital for preparing data for analysis and machine learning models. 𝐌𝐨𝐧𝐭𝐡 7: 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐅𝐨𝐜𝐮𝐬: Learn how to deploy machine learning models into production. This involves understanding tools and frameworks like Flask, Docker, and cloud services (AWS, Azure, Google Cloud). 𝐌𝐨𝐧𝐭𝐡 8: 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐨𝐜𝐮𝐬: Delve into deep learning concepts and frameworks such as TensorFlow and PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). 𝐌𝐨𝐧𝐭𝐡 9: 𝐂𝐕/𝐍𝐋𝐏 (𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧/𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠) 𝐅𝐨𝐜𝐮𝐬: Specialize in areas like computer vision and natural language processing. Learn techniques for image and text data processing and analysis. 𝐌𝐨𝐧𝐭𝐡 10: 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Prepare for data science interviews by practicing coding problems, understanding common data science interview questions, and refining your problem-solving skills. 𝐌𝐨𝐧𝐭𝐡 11: 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐑𝐞𝐬𝐮𝐦𝐞 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐅𝐨𝐜𝐮𝐬: Work on personal or open-source projects to build a portfolio. Prepare your resume to highlight your skills, projects, and achievements in data science. 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐄𝐧𝐝 𝐆𝐨𝐚𝐥: Achieve proficiency in data science, making you ready for a career in the field. This roadmap ensures a structured learning path, covering all necessary skills and tools. 𝐋𝐞𝐚𝐫𝐧 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐓𝐞𝐜𝐡𝐯𝐢𝐝𝐯𝐚𝐧:-https://lnkd.in/d46Eqtux Stay tuned for more! Follow, like, share, and repost this post.

How you can be a good data scientist? This roadmap will guide you in the best way possible.

𝐏𝐲𝐭𝐡𝐨𝐧 𝐰𝐢𝐭𝐡 𝐓𝐡𝐞𝐬𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬!!! Learn Python Programming :- https://lnkd.in/dp6EwDKY 1️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐏𝐚𝐧𝐝𝐚𝐬 = 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 Easily clean, transform, and analyze your data. Pandas offers robust data structures like DataFrames, making it essential for any data analyst. 2️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 = 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Build and train neural networks efficiently. TensorFlow provides a comprehensive platform for developing and deploying machine learning models. 3️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 = 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Create informative and attractive visualizations. Matplotlib is the foundation for many plotting libraries, helping you turn data insights into charts and graphs. 4️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐃𝐣𝐚𝐧𝐠𝐨 = 𝐖𝐞𝐛 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 Develop robust and scalable web applications quickly. Django’s high-level framework supports rapid development and clean design. 5️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐅𝐥𝐚𝐬𝐤 = 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 Build lightweight web applications and APIs. Flask is a micro-framework that offers simplicity and flexibility for small to medium-sized projects. 6️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐍𝐋𝐓𝐊 = 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 Process and analyze text data. NLTK (Natural Language Toolkit) provides tools for working with human language data, supporting tasks like classification and tokenization. 7️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐍𝐮𝐦𝐏𝐲 = 𝐍𝐮𝐦𝐞𝐫𝐢𝐜𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 Perform mathematical and statistical operations with ease. NumPy’s powerful n-dimensional array objects are essential for scientific computing. 8️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐒𝐜𝐢𝐤𝐢𝐭-𝐋𝐞𝐚𝐫𝐧 = 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Implement machine learning algorithms easily. Scikit-Learn provides efficient tools for data mining and analysis, built on NumPy, SciPy, and Matplotlib. 9️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬 = 𝐇𝐓𝐓𝐏 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬 Simplify HTTP requests. Requests allow you to send HTTP/1.1 requests, providing a wide range of functionalities to interact with web services. 🔟 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐏𝐲𝐓𝐨𝐫𝐜𝐡 = 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Similar to TensorFlow, PyTorch offers a dynamic computational graph and user-friendly API, making it popular among researchers for deep learning tasks. 1️⃣1️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 + 𝐒𝐜𝐫𝐚𝐩𝐲 = 𝐖𝐞𝐛 𝐒𝐜𝐫𝐚𝐩𝐢𝐧𝐠 Efficiently extract data from websites. Scrapy is an open-source framework for web crawling, used to gather and process data from websites.