Start with this “starter pack”:

• Jupyter Notebook or Google Colab for interactive experiments.

• NumPy and pandas for numerical work and data manipulation.

• matplotlib for basic plots and visualizations.

• scikit-learn for classical ML (regression, classification, clustering).
  Deep learning frameworks can wait until these feel comfortable.

Choose one small, well-defined use case (e.g. failure prediction, signal classification, anomaly detection).
List what data you already have or could log with minimal effort.
Start with simple, interpretable models (linear models, decision trees, random forests) to get a baseline.
Focus on whether it improves your current workflow, not on being cutting-edge.

Yes, but your options change.
You can use pretrained models (for images, text, etc.) and fine-tune slightly with your small dataset.
You can generate synthetic data from simulations, physical models, or existing tools.
You can also focus on simpler models that need fewer samples and encode strong domain assumptions.

Training: learning the model parameters from data, often from scratch.
Fine-tuning: starting from a pretrained model and adapting it to your specific data/task.
Inference: using an already trained model to make predictions on new inputs.
In many practical engineering setups, you mostly do inference and occasional fine-tuning, not full training.

Supervised learning: you have inputs and known outputs (labels); you learn to predict the outputs.
Unsupervised learning: you only have inputs; you look for structure (clusters, embeddings).
Reinforcement learning: an agent learns by trial and error using rewards.
Most engineering applications start and live mainly in supervised learning.

Always measure performance on data the model has not seen (test/validation sets).
Pick metrics that match the real problem (MSE, MAE, accuracy, precision/recall, etc.).
Compare against a simple baseline (mean predictor, linear regression, simple rules).
Then ask: is this error acceptable in my real system (cost, risk, safety), not just mathematically small?

If the model affects safety, money, or legal decisions, yes, you should.
Prefer simpler models when possible, or add interpretability tools (feature importance, SHAP, partial dependence).
In many engineering cases, a slightly less accurate but more explainable model is the safer choice.
Aim to be able to justify model decisions to a non-ML colleague.

Probably not; think of AI/ML as a complement, not a replacement.
ML is strong where explicit models are hard (complex patterns, messy data).
Classical tools excel when physics and theory are well understood.
Hybrid approaches (simulations + ML, model-based + data-driven) are often the most powerful.

• Using very complex models with tiny datasets → overfitting.

• Evaluating only on training data, not on a proper test set.

• Data leakage (letting test information sneak into training).

• Focusing only on accuracy while ignoring constraints (latency, safety, cost).

• Not tracking code/data versions, so results can’t be reproduced.

• Wrap the model as a function or service that takes inputs and returns outputs.
  Typical patterns: a Python script, a REST API, or a component in your existing pipeline/toolchain.
  At first, a simple script reading/writing files is enough.
  Later, you can add logging, monitoring, and proper deployment infrastructure.

• Expect missing values, outliers, wrong labels, and weird formats—it’s normal.
  Start by exploring with simple plots and summary stats.
  Handle missing data explicitly (drop, impute, or encode “unknown”), and document your choices.
  Often, 60–80% of the time in an ML project is data cleaning, not model training.

• Tabular data (rows = samples, columns = features) is the easiest starting point.
  CSV/Parquet are common; relational databases are fine too as long as you can extract tables.
  For signals/images/time series, you often convert them into features or consistent tensors.
  Whatever you choose, make it easy for someone else to re-load and understand your data.

• Feature engineering is often more important than choosing between two fancy models.
  Good features encode domain knowledge (ratios, aggregated statistics, derived indicators).
  Start with simple models and invest effort in better features, then upgrade models if needed.
  A simple model with good features often beats a complex model with raw data.

• Use deep learning when your data is high-dimensional and structured: images, audio, text, complex time series.
  For small tabular datasets, classical models (trees, linear, gradient boosting) are usually simpler and competitive.
  Deep learning needs more data, more compute, and more tuning.
  Don’t reach for it just because it’s fashionable—use it when it clearly fits the data type and scale.

• Don’t try to understand every equation at first.
  Focus on: problem setup, data used, high-level method, key results, and limitations.
  Ask: does this solve a problem similar to mine, and is the complexity worth it?
  If yes, look for code or implementations before trying to re-derive everything.

• Use version control (e.g. Git) for code and configuration.
  Fix random seeds when possible, or at least document them.
  Keep track of data versions or snapshots, not just “data.csv”.
  Store the environment details (library versions) or use containers so others can rerun your experiments.

• When you have a clear, reliable analytic formula or a simple rule that works well.
  When you have almost no data and no way to collect it.
  When the problem is heavily constrained by regulation and you cannot explain or audit the model.
  When the cost of errors is extremely high and you cannot tolerate uncertainty.