Machine Learning & Artificial Intelligence | Data Science Free Courses

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 😄👍

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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!👍✌️

Fastest way to excel at Data Interviews: Take as many Interviews as possible. Don't be too picky with the roles you apply for as a beginner. Cast a wide net and apply for every data-related position you can find. What's the worst that could happen? You might get rejected. So what? Remember: ☑ Each interview is a learning opportunity ☑ You'll refine your coding skills with every technical round ☑ Your data visualization explanations will get clearer each time ☑ You'll get more comfortable discussing your projects and impact. There are 2 types of data enthusiasts out there: Those who ace data analyst interviews and those who don't apply enough. 💡 Pro Tip: Keep an "interview journal" to note what worked, what didn't, and areas for improvement. Your future self will thank you! I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

How to enter into Data Science 👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation. 👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it. 👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊

Let's see the step-by-step process of Machine Learning! Step 1: Define the Problem Start by identifying the problem you wish to solve. Set clear goals and establish criteria for measuring success. Understanding the problem thoroughly is pivotal for the project's success. Step 2: Acquire and Explore Data Collect relevant data pertinent to the identified problem. Delve into the data to comprehend its characteristics, quality, and interrelationships. This preliminary analysis lays the groundwork for subsequent model development. Step 3: Prepare the Data Cleanse the data, address missing values, and engineer new features as necessary. This preprocessing phase ensures that the data is primed for training machine learning models. Step 4: Select and Train Models Choose suitable machine learning algorithms and train multiple models. Evaluate their performance using diverse techniques to identify the most effective approach. Step 5: Evaluate Models and Enhance Performance Assess the performance of trained models using various evaluation metrics. Fine-tune model parameters to optimize performance and iteratively enhance results. Step 6: Deployment Prepare the trained model for deployment into production. Collaborate closely with relevant teams to ensure seamless integration and performance monitoring. Step 7: Monitoring and Maintenance Continuously monitor the deployed model's performance in real-world scenarios. Regularly update and retrain the model with new data to maintain accuracy and relevance. Step 8: Documentation and Reporting Document the entire project, including methodologies, findings, and insights. Comprehensive documentation ensures transparency and facilitates the reproducibility of the project. I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

7 things you should know before becoming a Data Scientist: 7/ Higher complexity solutions =/= higher impact solutions. 6/ The best Data Scientists do much more than Data Science. They lead product teams, they talk to customers, they build pipelines etc. 5/ You won’t get along with every business partner. But you have to learn how to work with them. 4/ A lot of Data Science work is tedious and boring and repetitive. 3/ You will spend so much more time on communication than you expect. 2/ Data quality is often more important than fancy algorithms. 1/ You’ll make mistakes, a lot of it. What matters more is how you recover and grow from them. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

How to get started with data science Many people who get interested in learning data science don't really know what it's all about. They start coding just for the sake of it and on first challenge or problem they can't solve, they quit. Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude. If you're among people who want to get started with data science but don't know how - I have something amazing for you! I created Best Data Science & Machine Learning Resources that will help you organize your career in data, from first learning day to a job in tech. Share this channel link with someone who wants to get into data science and AI but is confused. 👇👇 https://t.me/datasciencefun Happy learning 😄😄

Data Analyst vs. Data Scientist 👇👇 https://t.me/sqlspecialist/775