Machine Learning & Artificial Intelligence | Data Science Free Courses

Learn Data Science in 2024 𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚 Pareto's Law states that "that 80% of consequences come from 20% of the causes". This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science. Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn. But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos). For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization. So, invest more time learning topics that provide immediate value now, not a year later. 𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡ There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly. Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books. So, find a mentor who can teach you practical knowledge in data science. 𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️ If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures. Join @datasciencefree for more ENJOY LEARNING 👍👍

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https://topmate.io/analyst/1024129 If you're a job seeker, these well structured document resources will help you to know and learn all the real time Data Science & Machine Learning Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide! Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

Machine learning engineers shouldn't only grow through years of hard work. Learning this way is too slow and complacent. You can grow much faster by actively: - Collaborating with talented professionals - Finding a mentor who is 2-5 years ahead - Taking on ambitious projects outside of your comfort zone Hard work can only take you so far, meanwhile leveraging a network and daring to take on challenges will 10x your growth. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

95% of Machine Learning solutions in the real world are for tabular data. Not LLMs, not transformers, not agents, not fancy stuff. Learning to do feature engineering and build tree-based models will open a ton of opportunities.

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Data analyst vs data scientist: - Data analysts analyse what has happened - Data scientists try to predict what will happen - Both use similar tools, but their focus differs. Visualisation is key for both, but more so for DAs as DS lean towards model building.

Important Pandas Methods

Job hunting? Your resume is your first impression—make it count! Don’t just list what you did or your responsibilities; showcase the impact you made. ❌ “Developed a ML model to predict customer churn.” ✅ “Built a churn prediction model using logistic regression, reducing churn by 12% and retaining $2M in quarterly revenue.” See the difference? One’s a task; the other’s a success. Employers want to see the value you bring, not just the work you’ve done. You would have heard the saying, “A single sheet of paper can’t decide my future,” but this single page can.😉 Remember, your resume isn’t just a record—it’s your professional life in a single page. I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

Essential Python Libraries to build your career in Data Science 📊👇 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Seaborn: - Statistical data visualization built on top of Matplotlib. 5. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 6. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 7. PyTorch: - Deep learning library, particularly popular for neural network research. 8. SciPy: - Library for scientific and technical computing. 9. Statsmodels: - Statistical modeling and econometrics in Python. 10. NLTK (Natural Language Toolkit): - Tools for working with human language data (text). 11. Gensim: - Topic modeling and document similarity analysis. 12. Keras: - High-level neural networks API, running on top of TensorFlow. 13. Plotly: - Interactive graphing library for making interactive plots. 14. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. 15. OpenCV: - Library for computer vision tasks. As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch. Free Notes & Books to learn Data Science: https://t.me/datasciencefree Python Project Ideas: https://t.me/dsabooks/85 Best Resources to learn Python & Data Science 👇👇 Python Tutorial Data Science Course by Kaggle Machine Learning Course by Google Best Data Science & Machine Learning Resources Interview Process for Data Science Role at Amazon Python Interview Resources Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING👍👍

Data Science Job Expectation VS Reality! Today, let's talk about real experiences working in data science. Sometimes, what we expect from a data science job may not match the reality of the day-to-day work. Let's explore this contrast between expectation and reality. 🎯Expectation: "I'll spend most of my time building fancy machine learning models and solving difficult problems." 📊Reality: While building and improving models is important, a big part of a data scientist's job is preparing and cleaning data. This involves organizing data, dealing with missing information, and making sure it's accurate. It requires attention to detail and careful work. 🎯 Expectation: "I'll work on groundbreaking projects that have a big impact." 📊 Reality: Data science projects often involve making small improvements and working step by step. You'll spend time analyzing data, finding patterns, and using data to make informed recommendations. Remember, many small wins can lead to significant positive outcomes. 🎯 Expectation: "I'll use the latest and coolest tools and technologies." 📊 Reality: While data scientists get to work with different tools and technologies, not every project needs the newest and trendiest ones. Depending on the project requirements, you may use well-established tools and focus more on solving problems rather than always exploring new technologies. 🎯 Expectation: "I'll work mostly with data." 📊Reality: Data science is a collaborative field. You'll work with people from different backgrounds, like experts in specific fields, engineers, and decision-makers. You'll need to understand business needs, share findings, and explain complex ideas to non-technical people. Communication and teamwork skills are important. 🎯Expectation: "I'll always be learning and keeping up with the latest research." 📊Reality: Learning is important, but it's also essential to balance staying updated with using existing knowledge effectively. The field changes quickly, so focusing on core concepts, gaining practical experience, and applying existing techniques to new problems are valuable skills. I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

Expectation

10 great Python packages for Data Science not known to many: 1️⃣ CleanLab Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. 2️⃣ LazyPredict A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code. 3️⃣ Lux A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data. 4️⃣ PyForest A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code. 5️⃣ PivotTableJS PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥 6️⃣ Drawdata Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. 7️⃣ black The Uncompromising Code Formatter 8️⃣ PyCaret An open-source, low-code machine learning library in Python that automates the machine learning workflow. 9️⃣ PyTorch-Lightning by LightningAI Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation. 🔟 Streamlit A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

【EU_Exchange】AI-GPT company recruits HR managers Only one phone is needed, work from home Age 25 and above Monthly salary 1800 to 5000 USD Main responsibilities: 1. Assist the company in recruiting personnel 2. Promote the company's AI smart products 3. Successful employment will receive  30+20 USD reward 4. Online customer service:https://chatlink.wchatlink.com/widget/standalone.html?eid=f55ff528d770d699c2cc389645f3577a&language=en