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

If you’re a junior AI engineer / undergrad worrying about how AI will affect your career, here are some tips for you: 1. Don’t skip the fundamentals of AI + CS Learn Python deeply, statistics, linear algebra, probability, data structures, algorithms, databases, and system design. Understand how machine learning models actually work — regression, classification, decision trees, neural networks, transformers. Learn how APIs, cloud systems, and distributed computing operate. Use AI as a tutor, but build real understanding yourself. 2. If you’re building something for the first time, do most of it yourself When learning your first model pipeline, chatbot, RAG app, ETL workflow, or ML deployment, write the code yourself as much as possible. Use AI for debugging help, explanations, and guidance — not as the autopilot. Struggling through setup, errors, and architecture decisions teaches lessons prompts never will. 3. Get good at reading outputs, prompts, and code fast Future AI engineers won’t just write code — they’ll review generated code, inspect model outputs, evaluate prompts, trace logs, and debug pipelines. Learn to quickly understand repositories, notebooks, APIs, and model responses. Reading systems efficiently will become a superpower. 4. Use AI to review your work constantly Have AI critique your datasets, model assumptions, prompt quality, architecture choices, code style, and deployment plans. Ask it what can break in production, where bias exists, how to optimize latency, or how to improve accuracy. Treat AI like a tireless mentor and second reviewer. 5. Don’t let AI replace your judgment AI can generate pipelines, recommend tools, and summarize tradeoffs — but you must decide what’s correct, scalable, ethical, and valuable. You still need to know when to use fine-tuning vs RAG, SQL vs NoSQL, batch vs streaming, open-source vs managed services. Judgment is where engineers create real value. 6. Build projects that solve real problems Anyone can clone a chatbot tutorial. Stand out by solving real business pain points: automate claims processing, build fraud detection, create scheduling systems, optimize operations, improve customer support. Real-world impact beats tutorial projects every time. 7. Learn deployment, not just notebooks Many juniors know how to train models but not ship them. Learn Docker, Amazon Web Services, Microsoft Azure, CI/CD, APIs, monitoring, logging, scaling, and security. Production AI engineers are far more valuable than notebook-only engineers. Follow: Santhosh Bandari

Stop over-engineering your AI projects from day one. If you want to move in the genAI world, "build to scale" is likely outdated advice. Here's why 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗶𝗻𝗴 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝘃𝗲𝗿 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 can save you time, resources, and ultimately lead to more successful AI implementations. 𝗧𝗟𝗗𝗥; 𝘐𝘯 𝘵𝘩𝘦 𝘢𝘨𝘦 𝘰𝘧 𝘨𝘦𝘯𝘈𝘐 𝘢𝘯𝘥 𝘤𝘭𝘰𝘶𝘥, 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘯𝘨 𝘺𝘰𝘶𝘳 𝘈𝘐 𝘪𝘥𝘦𝘢 𝘸𝘪𝘵𝘩 𝘢 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦 𝘤𝘭𝘰𝘶𝘥 𝘣𝘢𝘴𝘦𝘥 𝘔𝘝𝘗 𝘰𝘷𝘦𝘳 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘤𝘰𝘮𝘱𝘭𝘦𝘹, 𝘱𝘳𝘦𝘮𝘢𝘵𝘶𝘳𝘦𝘭𝘺 𝘴𝘤𝘢𝘭𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮. I was a firm believer in "build to scale", I really was. But with today's cloud-native architectures and the super fast iteration cycles enabled by genAI, this is changing. You do not need a monolith designed for millions of users on day one. You need to prove your AI-powered idea actually works. Whether it works is determined 20% by the way you engineered it and 80% the feedback your users give you (implicit or explicit). GenAI success is determined by adoption and not technology. Last year I built genAI applications that are loved by 5,000+ distinct enterprise users and here's the new playbook: 𝟭) 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗳𝗶𝗿𝘀𝘁. Build a lean, mean Minimum Viable Product (MVP) focused on validating your core hypothesis. Does your genAI solution actually solve a problem for your target audience? 𝟮) 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗰𝗹𝗼𝘂𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Use cloud platforms (AWS, Azure, GCP) and their serverless offerings (e.g. Cloud Run) to handle initial user loads. These services scale up and down automatically based on demand, will cost you very little and will carry you a long way on simple architectures and implementations. 𝟯) 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜. GenAI can significantly speed up your initial development, allowing you to get to market faster and start gathering real-world data. Your learning model is probably more important than your inference model in the beginning. In the last 12 months we had half a dozen MVPs where we released new versions every other day! 𝟰) 𝗦𝗰𝗮𝗹𝗲 𝘀𝗺𝗮𝗿𝘁 𝗮𝗻𝗱 𝗻𝗼𝘁 𝗽𝗿𝗲𝗺𝗮𝘁𝘂𝗿𝗲𝗹𝘆. Once you've validated your concept and see traction, then – and only then – start optimizing for larger-scale performance. Use the data you've gathered to inform your architectural decisions. That's what great product management is all about. 𝟱) 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿 𝘄𝗵𝗲𝗻 𝗻𝗲𝗲𝗱𝗲𝗱. Don't be afraid to refactor your architecture as you grow. Cloud-native architectures are designed for this kind of flexibility. You can rewrite parts of your application to improve performance and handle higher user loads without having to start from scratch.  Are you still building to scale from the start? What are your experiences with a "validate first" approach in your AI projects? Share your thoughts in the comments please!

How can statisticians and statistical programmers get started with hands-on AI? Here are some steps and recommendations: First, brush up on core concepts in ML including supervised and unsupervised learning, model evaluation, data preprocessing and target exactly how AI works with a particular use case. While statistics provides the theoretical backbone for many AI algorithms, practical ML involves new tools and workflows. Consider online courses like Coursera’s “Machine Learning” or DataCamp’s “Supervised Learning with scikit-learn,” which bridge the gap between statistical theory and ML practice. Programmers may need to explore and understand Python and R libraries such as scikit-learn, TensorFlow, and caret to get comfortable with some hands-on model building until LLMs get better with SAS. Next, focus on data wrangling, visualizations and feature engineering to prepare and interpret data, since high quality data is essential for successful AI projects. Courses like “Data Science: Data to Insights” (edX) or Kaggle’s hands-on tutorials offer some practical experience. Don’t overlook the importance of version control, reproducibility, and collaboration tools/skills that are critical in AI development. Finally, once you are comfortable seek out AI-focused communities, hackathons, and open-source projects. Engaging with forums like Stack Overflow, joining AI meetups, webinars or contributing to open-source repositories on GitHub can provide invaluable learning and networking opportunities. As a leader or manager, it is essential to possess a thorough understanding of relevant concepts and processes to effectively guide your team. For those who prefer a hands-on approach, a more comprehensive understanding is necessary. If you find any step overwhelming, take a step back and expand your learning before moving forward. Remember this not a race, it’s a marathon…By investing in targeted training and hands-on practice, statisticians and statistical programmers can confidently transition into the AI space and become key drivers of AI innovation in your organizations. Embrace continuous learning…

Training the model was easy, reproducing it in production is where the real chaos begins. Every ML project starts the same way. Someone says, “Let’s build a model.” A few weeks later the model works beautifully in a notebook. Metrics look great. Everyone is excited! Then you try to deploy it. Suddenly you are juggling half a dozen tools. Experiments live in MLflow. Data is versioned in DVC. Pipelines run through Kubeflow or Apache Airflow. The model might come from Hugging Face. Training happens somewhere else entirely. Individually, these tools are great. Together, they often feel like a group project where nobody talked to each other. So here are a few tips that worked well for me (so far) - First, persist the entire preprocessing pipeline together with the model. The artifact that leaves training should include feature transformations, schema, and any data normalization logic. Inference should load this bundle directly instead of trying to recreate preprocessing separately. Second, freeze the environment. Package the training environment in a container so the same dependencies and library versions are used during inference. If the environment changes between training and serving, subtle prediction differences are almost guaranteed. Third, treat the output of training as a versioned artifact. Use systems like MLflow or DVC not just to log metrics, but to register the exact model, data snapshot, and configuration that produced it. Finally, design the pipeline so deployment simply promotes an artifact that already passed evaluation. The deployment system should not need to rediscover preprocessing steps, dependency versions, or feature definitions. It should just load the artifact and serve it. Let me know if you have more suggestions in the comments below.

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

What does an 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 look like? MLOps practices are there to improve Machine Learning Product development velocity, the biggest bottlenecks happen when Experimentation Environments and other infrastructure elements are integrated poorly. Let’s look into the properties that an effective Experimentation Environment should have. As a MLOps engineer you should strive to provide these to your users and as a Data Scientist, you should know what you should be demanding for. 𝟭: Access to the raw data. While handling raw data is the responsibility of Data Engineering function, Data Scientists need the ability to explore and analyze available raw data and decide which of it needs to be moved upstream the Data Value Chain (2.1). 𝟮: Access to the curated data. Curated data might be available in the Data Warehouse but not exposed via a Feature Store. Such Data should not be exposed for model training in production environments. Data Scientists need the ability to explore curated data and see what needs to be pushed downstream (3.1). 𝟯: Data used for training of Machine Learning models should be sourced from a Feature Store if the ML Training pipeline is ready to be moved to the production stage. 𝟰: Data Scientists should be able to easily spin up different types of compute clusters - might it be Spark, Dask or any other technology - to allow effective Raw and Curated Data exploration. 𝟱: Data Scientists should be able to spin up a production like remote Machine Learning Training pipeline in development environment ad-hoc from the Notebook, this increases speed of iteration significantly. 𝟲: There should be an automated setup in place that would perform the testing and promotion to a higher env when a specific set of Pull Requests are created. E.g. a PR from feature/* to release/* branch could trigger a CI/CD process to test and deploy the ML Pipeline to a pre-prod environment. 𝟳: Notebooks and any additional boilerplate code for CI/CD should be part of your Git integration. Make it crystal clear where a certain type of code should live - a popular way to do this is providing repository templates with clear documentation. 𝟴: Experiment/Model Tracking System should be exposed to both local and remote pipelines. 𝟗: Notebooks have to be running in the same environment that your production code will run in. Incompatible dependencies should not cause problems when porting applications to production. It can be achieved by running Notebooks in containers. Did I miss something? 👇 #GenAI #LLM #LLMOps #MachineLearning

🚀 Machine Learning is more than just importing a library and calling .fit()! . . . . Many think the magic is just picking an algorithm, but the real win comes from mastering the nuances of data and training. The ML Performance Essentials ✅ Prevent Overfitting: Don't let your model memorize noise. Use L1/L2 regularization to penalize complexity and improve generalization. ✅ Balance Bias & Variance: Simple models underfit (high bias), while complex models overfit (high variance). The goal is the "sweet spot" of low both. ✅ Handle Imbalanced Data: Accuracy is a trap if 99% of your data is one class. Use SMOTE for oversampling and track F1-scores instead. ✅ Leverage Ensembles: Combine models to boost results. Bagging reduces variance (Random Forest), while Boosting reduces bias (XGBoost). ✅ Prioritize Interpretability: A "black box" won't cut it in regulated fields. Use SHAP or LIME to explain individual predictions. A robust model is one that is reliable and explainable, not just one with a high accuracy score on training data. Which do you prioritize in your projects: raw accuracy or model interpretability? 👉 Follow Sarveshwaran Rajagopal for more insights on AI, LLMs & GenAI. 🌐 Learn more at: https://lnkd.in/d77YzGJM #AI #MachineLearning #DataScience #MLOps #Python #XGBoost #DeepLearning

For predictive analytics, you need a basic understanding of machine learning. Here’s your roadmap to build your first ML models: 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: Grasp the business problem your stakeholders need to solve based on the model's output. The requirements you gather will be your guideline for the next steps. 2. 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Identify and gather the relevant input data for your model. Clean the data by handling missing values and ensuring it's formatted correctly. This preparation is important for the quality of the model, as messy data can easily lead to a "garbage in, garbage out" situation. 3. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Transform raw data into features that better represent the underlying problem to the predictive models. This enhances the model's ability to understand the data. Decompose timestamps or encode categorical columns to make the data more digestible for your model. 4. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Select the most relevant features that contribute to the model's predictive power. Not every feature improves your model. 5. 𝗧𝗿𝗮𝗶𝗻 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹: Train your model using your prepared dataset. Try different algorithms as deep learning isn’t always the best solution. Often simpler algorithms like linear regression or decision trees can provide good results and are more transparent in how they make predictions. 6. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Evaluate your model’s performance using appropriate metrics and tune the model's parameters. Fine-tuning is important for improving accuracy and reliability. Be ready to iterate on data preparation and feature handling if performance is lacking. 7. 𝗗𝗲𝗽𝗹𝗼𝘆 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹: When your model performs well enough, deploy it to start delivering insights and predictions in a real-world environment. Deployment is where your model starts creating value for the business. 8. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹: Monitor your model's performance over time to catch any growing deviation in predictions and changes in data patterns or business requirements. Continuous monitoring and adjustments ensure that your model remains accurate and relevant for the business. Machine learning is often associated with the realm of data science, but data and business analysts also benefit from having basic ML knowledge. Whether you're preparing training datasets or moving into predictive analytics, understanding these steps will help to grow your role. Are you currently using machine learning or planning to add it to your skill set? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #machinelearning #careergrowth