What Recent AI Layoffs Mean for Machine Learning Freshers

import csv
from collections import Counter

# Fill this from listings you read each week (no scraping needed)
listings = [
    {"title": "ML Engineer (Junior)", "skills": "python;sql;docker;mlops"},
    {"title": "Data Scientist", "skills": "python;sql;statistics"},
    {"title": "AI Application Developer", "skills": "python;apis;evaluation"},
    {"title": "Data Analyst (ML team)", "skills": "sql;python;dashboards"},
]
counts = Counter(s for l in listings for s in l["skills"].split(";"))
for skill, n in counts.most_common():
    print(f"{skill:12} {n}")

from datetime import date

entry = {
    "week_of": date(2026, 11, 2).isoformat(),
    "listings_read": 12,
    "new_skill_seen": "model evaluation",
    "applications_sent": 5,
    "project_progress": "added API to churn model",
    "mood_1_to_5": 3,
}
for k, v in entry.items():
    print(f"{k:18} {v}")
# Tracking facts each week reduces anxiety from headlines.

import pandas as pd
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=1000, n_features=5, random_state=7)
df = pd.DataFrame(X, columns=[f"f{i}" for i in range(5)])
X_tr, X_te, y_tr, y_te = train_test_split(df, y, random_state=7)

m = LogisticRegression().fit(X_tr, y_tr)
res = X_te.copy()
res["true"], res["pred"] = y_te, m.predict(X_te)
errors = res[res["true"] != res["pred"]]
print("error rate:", round(len(errors) / len(res), 3))
print(errors.describe().loc[["mean"]].round(2))   # where do errors cluster?

# pip install fastapi uvicorn scikit-learn joblib
# Run: uvicorn app:app --reload
import joblib
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()
model = joblib.load("model.joblib")

class Features(BaseModel):
    values: list[float]

@app.get("/health")
def health():
    return {"status": "ok"}

@app.post("/predict")
def predict(f: Features):
    return {"prediction": int(model.predict([f.values])[0])}

import re

def keywords(text):
    return set(re.findall(r"[a-z][a-z+#.]+", text.lower()))

listing = "Junior ML engineer: Python, SQL, Docker, model evaluation, FastAPI"
resume = "Python, SQL, scikit-learn, FastAPI, model evaluation, Git"

missing = keywords(listing) - keywords(resume) - {"junior", "ml", "engineer:"}
print("In listing but not in resume:", sorted(missing))
# Add only skills you genuinely have; learn the rest.

WEEKLY = {
    "Mon": "Read 10 listings, update tracker",
    "Tue": "Project work: evaluation or deployment",
    "Wed": "3 tailored applications",
    "Thu": "Project work + README",
    "Fri": "2 tailored applications + 1 networking message",
    "Sat": "Interview practice",
    "Sun": "Rest",
}
for day, task in WEEKLY.items():
    print(f"{day}: {task}")

Weeks 1-3   Strengthen SQL, statistics and evaluation
Weeks 4-6   Project 1: model with baseline, error analysis, API
Weeks 7-9   Project 2: evaluated pretrained-model feature
Weeks 10-12 Portfolio polish, tailored applications, interview practice

Every week: read listings and update your skill tracker.

Primary:   Junior ML Engineer, Junior Data Scientist
Adjacent:  Data Analyst (ML team), Data Engineer, AI App Developer
Sectors:   finance, retail, healthcare, logistics, education, IT services

Apply to a mix every week.

Healthy search habits

- Limit doom-scrolling layoff news to once a day
- Track progress you control: skills, projects, applications
- Talk to peers or mentors weekly
- Take one full rest day
- Remember: a slow market is not a judgement on you