Listen to this Post
This 8-week roadmap guides you through building a Machine Learning (ML) model from scratch, covering Python, data preprocessing, supervised/unsupervised learning, deep learning, and deployment.
Week 1: Python & Math Foundation
- Python Basics: Variables, loops, functions, and libraries (NumPy, Pandas).
- Math Essentials: Linear algebra, probability, and statistics.
- Data Visualization: Matplotlib and Seaborn for plotting.
You Should Know:
Install Python libraries
pip install numpy pandas matplotlib seaborn
Basic NumPy array operations
import numpy as np
arr = np.array([1, 2, 3])
print(arr 2) Output: [2 4 6]
Pandas DataFrame
import pandas as pd
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
print(df.head())
Week 2: Data Wrangling & Preprocessing
- Handling Missing Data: Imputation techniques.
- Feature Scaling: Normalization vs. Standardization.
- Exploratory Data Analysis (EDA): Statistical summaries, correlation matrices.
You Should Know:
Handling missing values df.fillna(df.mean(), inplace=True) Standardization with Scikit-learn from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaled_data = scaler.fit_transform(df)
Week 3: Supervised Learning – Regression
- Linear, Ridge, Lasso Regression
- Model Evaluation: MAE, MSE, R².
You Should Know:
Linear Regression Example from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test)
Week 4: Supervised Learning – Classification
- Logistic Regression, KNN, Decision Trees
- Evaluation Metrics: Confusion matrix, precision, recall.
You Should Know:
Logistic Regression from sklearn.linear_model import LogisticRegression clf = LogisticRegression() clf.fit(X_train, y_train)
Week 5: Unsupervised Learning
- K-Means Clustering, PCA
- Dimensionality Reduction
You Should Know:
K-Means Clustering from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=3) kmeans.fit(data)
Week 6: Model Optimization
- Cross-Validation, GridSearchCV
- Feature Engineering
You Should Know:
GridSearchCV for hyperparameter tuning
from sklearn.model_selection import GridSearchCV
params = {'n_neighbors': [3, 5, 7]}
grid = GridSearchCV(KNeighborsClassifier(), params)
grid.fit(X_train, y_train)
Week 7: Deep Learning Basics
- Neural Networks with TensorFlow/Keras
- Digit Recognition (MNIST Example)
You Should Know:
Simple Neural Network import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
Week 8: Capstone Project
- Deploy ML model (Flask/FastAPI)
- GitHub & LinkedIn Showcase
You Should Know:
Flask API for ML model pip install flask
from flask import Flask, request, jsonify
app = Flask(<strong>name</strong>)
@app.route('/predict', methods=['POST'])
def predict():
data = request.json
prediction = model.predict([bash])
return jsonify({"prediction": prediction.tolist()})
What Undercode Say
This roadmap ensures a structured approach to ML mastery. Key takeaways:
– Linux/CLI Commands:
Monitor system resources while training models top -i nvidia-smi GPU monitoring
– Windows/WSL for ML:
wsl --install Enable Linux on Windows
– Git for Version Control:
git init git add . git commit -m "Initial ML project commit"
Expected Output:
A fully functional ML model deployed with Flask, documented on GitHub, and showcased on LinkedIn.
Further Resources:
References:
Reported By: Manali Kulkarni – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
