Tips for Machine Learning Success

I taught myself machine learning > 10 years ago. If I had to start again today, I wouldn’t touch models, LLMs, or agents first, as many AI experts suggest. I'd start with the math and the code. Ugly truth: 90% of people skip the foundations, then wonder why everything feels like magic or falls apart in production. If you want to be different, actually understand ML, not just copy-paste, this is the roadmap I'd follow: Start with fundamentals: Because no matter how fast LLMs or GenAI evolve, your math, code, and logic will keep you relevant. Here's what you should focus on: 📐 1. Linear Algebra Learn these core ideas: Vectors, matrices, tensors Matrix multiplication (dot products, broadcasting) Transpose, inverse, rank, determinants Eigenvalues & eigenvectors (especially for PCA & embeddings) Projections and orthogonality ✅ Use NumPy to implement everything yourself → Practice matrix ops, dot products, and visualizing transformations with Matplotlib 🔁 2. Calculus Focus on: Derivatives & partial derivatives Chain rule (for backpropagation in neural nets) Gradient descent Convex functions, minima/maxima ✅ Use SymPy or JAX to visualize and compute derivatives → Plot functions and their gradients to develop deep intuition 🎲 3. Probability You need a solid grip on: Random variables (discrete & continuous) Conditional probability & Bayes' rule Joint & marginal probability The Chain rule Expectation, variance, entropy Common distributions: Bernoulli, Binomial, Gaussian, Poisson Central limit theorem The law of large numbers ✅ Simulate simple probability experiments in Python with NumPy → E.g. simulate sampling from distributions 📊 4. Statistics These are must-know topics: Descriptive stats: mean, median, mode, standard deviation Hypothesis testing: p-values, confidence intervals, t-tests Correlation vs. causation Sampling, bias, and variance Overfitting/underfitting A/B testing basics ✅ Use Pandas & SciPy to explore real datasets → Calculate descriptive stats, create histograms/box plots, run t-tests 🔧 Essential Python libraries to learn early NumPy – for vectorized math and fast array ops Pandas – for loading, cleaning, and analyzing tabular data Matplotlib / Seaborn – for plotting and visualizing distributions, relationships, and trends SymPy – for symbolic math and calculus SciPy – for stats, optimization, and numerical methods Use Jupyter Notebooks(to combine math, code, & visuals in one place) 📚 Best resources to nail the fundamentals: ✅ Machine Learning Foundations Math series (ML Foundations: Linear Algebra, Calculus, Probability, and Statistics)-series of 4 courses that I've created together with LinkedIn learning ✅ Hands-On ML with TensorFlow & Keras book by Aurélien Géron ✅ The Hundred-page Machine Learning Book by Andriy Burkov If you want to become an actual ML engineer, not just someone who watches and copies demos, start here. ♻️ Repost to help others💚

Machine Learning students try more complex ML models when they wanna improve their results. So they miss the elephant in the room 🐘 ↓ A Machine Learning model is like a cake, with 2 main ingredients: → a dataset → an ML algorithm, for example, linear regression, or XGBoost. And the thing is, no matter what algorithm you choose, 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 𝗰𝗮𝗻 𝗼𝗻𝗹𝘆 𝗯𝗲 𝗮𝘀 𝗴𝗼𝗼𝗱 𝗮𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘆𝗼𝘂 𝘂𝘀𝗲𝗱 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻 𝗶𝘁. The problem is that in online courses, and ML competitions, you work with a 𝗳𝗶𝘅𝗲𝗱 dataset that someone has generated for you. In real-world projects, there is no dataset waiting for you. Instead, you need to 𝗰𝗿𝗲𝗮𝘁𝗲 it. And this is the most critical step in the whole project. Most ML problems in the real world are solved in a supervised manner, which means your dataset contains: → a collection of 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, that serve as inputs to your model → a 𝘁𝗮𝗿𝗴𝗲𝘁 metric you want to predict, aka the model output. ✅ Useful features bring information and signal relevant to the target you want to predict. ❌ Useless features are just noise, and add no value to your ML model, no matter how complex your algorithm is. → Adding a useful feature to your model is the best way to improve it. 🏆 → Adding two useful features works even better. 🏆🏆 → And having 3 of them is a blessing. 🏆🏆🏆 To add new useful features, you need to → think beyond the data available right now at the data warehouse. → talk to senior colleagues who have context about the business. → think outside of the box you put yourself into after 3 weeks of working on the model. You often find pieces of information, relevant to the problem, that are scattered in the company's IT systems, or maybe outside on a third-party vendor, which will greatly help your model. 𝗧𝗼 𝘀𝘂𝗺 𝘂𝗽: → in real-world ML, the dataset is not set in stone. YOU have the power to expand it. → adding useful features to your dataset is the best way to improve your model. → improving ML models in the real world is more about data engineering than fancy ML models. ---- Hi there! It's Pau Labarta Bajo 👋 Every day I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #mlops #realworldml

90% of ML projects never make it to production. Here's the 8-step framework that works. 𝐒𝐭𝐞𝐩 𝟏: 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 ↳ Start with WHY, not HOW ↳ Is ML even the right solution? ↳ Define success criteria upfront 𝐒𝐭𝐞𝐩 𝟐: 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 ↳ Check data quality: missing values, duplicates, outliers ↳ EDA: distributions, correlations, patterns ↳ Document your data sources and limitations 𝐒𝐭𝐞𝐩 𝟑: 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 ↳ Handle missing values (imputation, dropping) ↳ Encode categorical variables ↳ Create new features from domain knowledge ↳ This alone can improve performance by 20-30% 𝐒𝐭𝐞𝐩 𝟒: 𝐓𝐫𝐚𝐢𝐧-𝐓𝐞𝐬𝐭 𝐒𝐩𝐥𝐢𝐭 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 ↳ Split: 70% train, 15% validation, 15% test ↳ Use stratified split for imbalanced data ↳ Never touch test data until final evaluation 𝐒𝐭𝐞𝐩 𝟓: 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 ↳ Start simple (logistic regression, decision tree) ↳ Try XGBoost, LightGBM, Random Forest ↳ Track experiments with MLflow or W&B 𝐒𝐭𝐞𝐩 𝟔: 𝐌𝐨𝐝𝐞𝐥 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 ↳ Use appropriate metrics (F1, ROC-AUC, RMSE) ↳ Analyze errors: confusion matrix, feature importance ↳ Does 85% accuracy actually solve the business problem? 𝐒𝐭𝐞𝐩 𝟕: 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 ↳ Build API endpoint (FastAPI, Flask) ↳ Containerize with Docker ↳ Deploy to cloud (AWS, GCP, Azure) 𝐒𝐭𝐞𝐩 𝟖: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 ↳ Track prediction accuracy over time ↳ Monitor for data drift and concept drift ↳ Retrain periodically with fresh data 𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝: ❌ Data leakage (using future info to predict past) ❌ Ignoring class imbalance ❌ Deploying without monitoring ❌ Optimizing metrics without business context 𝐏𝐫𝐨 𝐭𝐢𝐩: Your first end-to-end project will be messy, that's normal. Focus on completing the full cycle, then iterate. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐋? Here are 5 resources I recommend: 1. Machine Learning by Andrew Ng - https://lnkd.in/diqSeD-k 2. Codebasics ML Playlist - https://lnkd.in/dBiYAeN7 3. Krish Naik ML Playlist - https://lnkd.in/dcpAS5gA 4. StatQuest with Joshua Starmer - https://lnkd.in/dhZ3aVhf 5. Sentdex ML Tutorials - https://lnkd.in/dCFPtDv8 Which step do you find most challenging? 👇 ♻️ Repost to help someone starting their ML journey

I spent 6 months learning ML the wrong way. Here's the roadmap I wish I had 👇 Most people start with theory and burn out. I did too. Here's what actually works: Month 1-2: Foundation (But Make It Practical) → Python + NumPy + Pandas → Learn stats by analysing real datasets (not textbooks) → Build a simple linear regression from scratch Hot take: Skip the heavy math until you need it. Month 3-5: Core ML Skills → Andrew Ng's course OR Fast.ai (pick one, finish it) → Implement algorithms without libraries first → Then use scikit-learn to see the difference The struggle teaches you more than the success. Month 6-7: Projects That Actually Matter This is where 90% fail. Don't build another Iris classifier. Build these instead: -> Recommendation system for something you love -> Sentiment analyser for product reviews -> Image classifier for a local problem Make it so good that recruiters notice. Month 8+: Specialise & Be Visible Pick ONE: NLP, Computer Vision, or Time Series Write about what you learn (yes, on LinkedIn) Contribute to one open-source ML project DM 5 ML engineers for coffee chats The Uncomfortable Truth: You'll apply to 50+ internships. You'll get rejected a lot. That's normal. Every "no" teaches you what to improve. My Challenge to You: Comment "I'm in" if you're starting this journey. Let's build a community of people actually doing the work. P.S. What's stopping you from starting today? Reply and let me know—I read every comment. #MachineLearning #MLInternship #DataScience #AIJobs #TechCareers #100DaysOfCode #MachineLearning #ArtificialIntelligence #DataScience #TechJobs #CareerAdvice #Internship #AIcommunity

The GPUs were top-tier. The models were solid. Training was still slow. The real problem? The data pipeline feeding them. GPU performance is rarely limited by compute alone. It’s limited by how efficiently data moves, loads, and synchronizes. Here’s the structured 10-step path 👇 Step 1: Define Target GPU Throughput Start by calculating samples per second per GPU and defining a minimum sustained throughput target. Design for steady performance, not peak spikes. Step 2: Co-Locate Compute and Data Keep data physically close to GPUs to reduce cross-rack traffic, latency variability, and east-west congestion that silently kills scaling. Step 3: Implement Multi-Level Caching Use layered caching - object storage, distributed cache, node-local SSD, and memory buffers - to keep GPUs continuously fed. Cold storage should never directly serve GPUs. Step 4: Parallelize Data Loading Increase data loader workers, enable asynchronous prefetching, and overlap I/O with compute. If GPUs wait for data, your scaling breaks. Step 5: Design for Distributed Synchronization Align shard distribution across training nodes, avoid duplicate reads, and balance partitions evenly to prevent gradient sync delays and network spikes. Step 6: Select the Right Storage Architecture Evaluate object storage for durability, distributed file systems for throughput, and NVMe for hot data. Hybrid storage layers outperform single-tier designs. Step 7: Optimize Data Format and Serialization Adopt columnar formats like Parquet, compress intelligently, and reduce decoding overhead. Inefficient serialization wastes more compute than expected. Step 8: Minimize CPU Bottlenecks Monitor CPU saturation, optimize preprocessing, and remove heavy Python loops. GPUs depend on CPUs to prepare data efficiently. Step 9: Map the Data Access Pattern Analyze sequential vs random reads, shuffle frequency, augmentation intensity, and batch size. Most inefficiencies come from misunderstood access patterns. Step 10: Monitor and Continuously Benchmark Track GPU utilization, data loader wait time, and end-to-end samples per second. You cannot optimize what you don’t measure. The core principle: Throughput > Theoretical FLOPS. AI performance is a pipeline problem, not just a hardware problem. If your GPUs aren’t hitting expected utilization, the bottleneck is probably upstream.

Are you comfortable using AI tools but want to understand what’s happening under the hood to design better systems? Here are some tips to get started: ✅ Start with the basics: Learn how algorithms — like decision trees, neural networks, or reinforcement learning — actually process data. You can pick the path that works best for you: free or paid online courses, tutorials, tech meetups, or a textbook. ✅ Pair theory with practice: Write small programs yourself instead of just relying on pre-built tools. For example, try coding a simple image classifier or a reinforcement agent in a game-like setting. ✅ Apply AI in your field: Focus on small but real use cases to stay motivated and inspired. A marketer might build a model to predict campaign performance, a mechanical engineer could create a sensor fault detector, a teacher could try a chatbot for student Q&A. ✅ Stay critical: Think about the assumptions built into models and how they influence decisions. Watch out for biased training data, results that seem too confident, or outputs that don’t generalize outside the test case.   Curious how others approached this transition: Did you go theory first (like me), or hands-on first?   #AI #MachineLearning #CareerGrowth #Engineering #Learning

If you're starting with machine learning, don't make the same mistakes I made. Here are 5 things I had to learn the hard way: 1) The technology doesn't matter. I would hop between C#, Java, Python, and R early in my ML journey. I read something online and think it would magically solve my problem if I used a different technology. I was 100% wrong on this. Pick a tech and stick with it. 2) I obsessed over algorithms. This one is an easy trap to fall into. Thinking that switching to a different ML algorithm will solve all your problems. Turns out that knowing a small number of useful algorithms really well is the key to success. For example, the mighty random forest. 3) I obsessed over tuning. To be clear, you must tune your ML models to get the best results. However, I was under the false assumption that tuning would yield significant gains with my ML models. In practice, you do tuning at the end of the project for incremental gains. 4) I didn't obsess over the data. There's a famous saying in data science: "Data trumps algorithm." In DIY data science, this is truth. Over the years, my best results were because I engineered the best features. Not because I used fancy algorithms. And not because of tuning. 5) I didn't dive into the business. This is closely related to the previous mistake. You must develop a sufficient understanding of business processes to engineer the best features. The best features produce the best models. By extension, business process knowledge produces the best models. I post daily about DIY data science topics like: SQL Excel Python Machine learning Give me a follow to learn more. Stay healthy and happy data sleuthing!

𝐅𝐫𝐨𝐦 𝐓𝐡𝐞𝐨𝐫𝐲 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 This guide explains how machine learning actually works beyond theory. It focuses on advanced training techniques, optimization strategies, and real-world deployment practices. From transfer learning to federated learning, it shows how models can learn efficiently even with limited or private data. It also highlights the importance of optimization methods like mixed precision and gradient techniques to improve speed and reduce memory usage. The guide covers regularization, model compression, and multi GPU strategies that make systems scalable. Most importantly, it emphasizes that small mistakes like poor data handling can impact performance significantly. The biggest takeaway is simple. Machine learning is not just about building models. It is about creating efficient, scalable, and production ready systems. 👉🏻follow Alisha Surabhi for more such content

So You Want to Learn Machine Learning? Here’s the Roadmap I Wish I Had. Recently, someone asked me: Richel, how do I start my journey into machine learning? I smiled because I’ve been there—excited, overwhelmed, and unsure where to begin. If I could sit down with my younger self, this is the exact step-by-step roadmap I’d share: ✅ Start with Python & SQL—these are your core tools for working with data. ✅ Master Data Analysis & Data Cleaning—No clean data, no good models. ✅ Build a strong foundation in statistics & probability—understand the math behind the magic. ✅ Learn Supervised & Unsupervised Learning—Start simple, then dive deeper. ✅ Work on real projects—hands-on practice is the fastest way to grow. ✅ Explore deep learning—but only when you’re ready. ✅ Follow a Structured Learning Path—consistency beats randomness every time. To make these even easier, I’ve turned these steps into a visual carousel—swipe through to see each step laid out clearly. If you’re starting your machine learning journey or thinking about switching into the field, I hope this helps you take your first confident step. What steps are you on right now? Let me know in the comments—I'd love to cheer you on! #MachineLearning #DataScience #CareerAdvice #Python #SQL #AI #LearningJourney #CareerGrowth #GraceAndGrowth