EXERCISE
1You now have a complete beginner's toolkit for machine learning — here is your inventory and the map forward.
You have gone from "what is ?" to building, evaluating, and deploying models and decision trees. That is not a small thing. Here is everything you now know — and where each skill fits in the bigger picture.
Your toolkit inventory:
| Skill | Python Tool | When to Use |
|---|---|---|
| Mean, Median, Mode | numpy.mean(), numpy.median(), scipy.stats.mode() | First look at any data set |
| Std Dev & Variance | numpy.std(), numpy.var() | Measuring spread and reliability |
| Percentiles | numpy.percentile() | Outlier detection, binning |
| Histograms | matplotlib.pyplot.hist() | See distribution shape |
| Scatter Plots | matplotlib.pyplot.scatter() | See relationships between variables |
| Linear Regression | scipy.stats.linregress() | Straight-line relationships |
| Polynomial Regression | numpy.polyfit() + numpy.poly1d() | Curved relationships |
| Multiple Regression | sklearn.linear_model.LinearRegression() | Multiple input variables |
| Feature Scaling | sklearn.preprocessing.StandardScaler() | Before feeding data to models |
| Train/Test Split | Manual split or sklearn.model_selection.train_test_split() | Evaluating model honestly |
| Decision Trees | sklearn.tree.DecisionTreeClassifier() | Classification problems |
The learning path ahead:
# Where to go next:
next_topics = [
"K-Nearest Neighbours (KNN)", # Classification
"Logistic Regression", # Binary classification
"Random Forests", # Better decision trees
"Neural Networks", # Deep learning
"Cross-Validation", # Better than single train/test
"Hyperparameter Tuning", # Optimising model settings
]
for i, topic in enumerate(next_topics, 1):
print(f"{i}. {topic}")
# Output:
# 1. K-Nearest Neighbours (KNN)
# 2. Logistic Regression
# 3. Random Forests
# 4. Neural Networks
# 5. Cross-Validation
# 6. Hyperparameter Tuning
Pro Tip: The gap between "course learner" and "ML practitioner" is not more algorithms — it is practice on messy real-world data. Kaggle competitions, personal projects, and work datasets will teach you more than any course. You now have the foundations. Go build things that matter.