AI/ML Learning Path — Math, Tools and Projects

In this tutorial, you'll learn about AI/ML Learning Path. We cover key concepts, practical examples, and best practices.

This AI/ML learning path takes you from mathematics and Python fundamentals through deep learning, NLP, computer vision, and MLOps — building production-ready machine learning systems used by companies like Doda Browser and Durga Antivirus Pro.

What You'll Learn

Why It Matters

Artificial intelligence is transforming every industry. From recommendation engines in e-commerce to threat detection in antivirus software, ML models are making decisions that affect millions of users. The global AI market is projected to reach $1.8 trillion by 2030, and ML engineers command salaries between $120,000 and $250,000.

Who This Is For

Software engineers transitioning into AI/ML, data analysts upgrading to machine learning, and students with undergraduate mathematics who want to build production ML systems. You need basic Python knowledge and high school-level math.

timeline
    title AI/ML Learning Path
    Phase 1 : Linear algebra : Calculus : Statistics : Python for ML
    Phase 2 : Supervised learning : Unsupervised learning : Model evaluation
    Phase 3 : Deep learning : NLP : Computer vision : Transformers
    Phase 4 : MLOps : Deployment : Monitoring : Ethics

Phased Learning Path

Phase 1: Mathematics and Python for ML (Weeks 1-4)

Linear Algebra (Week 1)

Vectors, matrices, matrix multiplication, eigenvalues and eigenvectors, singular value decomposition, principal component analysis. Implement these from scratch using NumPy to build intuition.

import numpy as np

# Matrix multiplication and eigendecomposition
A = np.array([[4, -2], [1, 1]])
eigenvalues, eigenvectors = np.linalg.eig(A)

print("Eigenvalues:", eigenvalues)
print("Eigenvectors:\n", eigenvectors)

# Expected output:
# Eigenvalues: [3. 2.]
# Eigenvectors:
#  [[ 0.89442719  0.70710678]
#   [ 0.4472136   0.70710678]]

Calculus and Optimization (Week 2)

Derivatives, partial derivatives, chain rule, gradient descent, stochastic gradient descent, learning rates, momentum, and Adam optimizer. Understand backpropagation conceptually before implementing it.

Probability and Statistics (Week 3)

Probability distributions (normal, binomial, Poisson), Bayes theorem, maximum likelihood estimation, hypothesis testing, p-values, confidence intervals, bias-variance tradeoff, and cross-validation. These are essential for model evaluation and interpretation.

Python for ML (Week 4)

Master pandas for data manipulation, NumPy for numerical computing, and Matplotlib and seaborn for visualization. Learn data cleaning, feature engineering, handling missing values, and exploratory data analysis on real datasets.

import pandas as pd
import matplotlib.pyplot as plt

# Load and explore a dataset
df = pd.read_csv('housing.csv')
print(df.head())
print(df.describe())
print(df.isnull().sum())

# Visualize correlations
correlation_matrix = df.corr()
plt.figure(figsize=(10, 8))
plt.imshow(correlation_matrix, cmap='coolwarm', aspect='auto')
plt.colorbar()
plt.show()

Phase 2: Core Machine Learning (Weeks 5-8)

Supervised Learning (Weeks 5-6)

Learn linear regression, logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), support vector machines, and k-nearest neighbors. Understand when to use each algorithm and how to tune hyperparameters.

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error
import numpy as np

# Train and evaluate a random forest model
model = RandomForestRegressor(
    n_estimators=200,
    max_depth=15,
    min_samples_leaf=5,
    random_state=42
)

scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')
rmse_scores = np.sqrt(-scores)
print(f"Cross-validation RMSE: {rmse_scores.mean():.4f} (+/- {rmse_scores.std() * 2:.4f})")

Unsupervised Learning (Week 7)

K-means clustering, hierarchical clustering, DBSCAN, t-SNE, and PCA for dimensionality reduction. Apply clustering to customer segmentation, anomaly detection, and image compression.

Model Evaluation and Feature Engineering (Week 8)

Confusion matrices, precision, recall, F1-score, ROC curves, AUC, feature importance, SHAP values, feature selection, and handling imbalanced datasets with SMOTE and class weights. Learn to build ML pipelines with sklearn's Pipeline class.

Phase 3: Deep Learning and Specializations (Weeks 9-12)

Neural Networks and Deep Learning (Week 9)

Learn PyTorch or TensorFlow with Keras. Build feedforward networks, understand activation functions (ReLU, sigmoid, tanh, softmax), loss functions, optimizers, batch normalization, dropout, and early stopping.

import torch
import torch.nn as nn

# Simple feedforward network in PyTorch
class MLP(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_classes)
        )
    
    def forward(self, x):
        return self.layers(x)

model = MLP(input_size=784, hidden_size=256, num_classes=10)
print(model)

Natural Language Processing (Week 10)

Tokenization, embeddings (Word2Vec, GloVe), RNNs, LSTMs, GRUs, attention mechanisms, and the Transformer architecture. Build a text classification model and a simple chatbot using pre-trained embeddings.

Computer Vision (Week 11)

CNNs, convolutional layers, pooling, data augmentation, transfer learning with ResNet and EfficientNet, object detection (YOLO, SSD), and image segmentation (U-Net). Fine-tune a pre-trained model on a custom dataset.

Transformers and LLMs (Week 12)

Understand the Transformer architecture from the "Attention Is All You Need" paper. Learn about BERT, GPT, T5, and Llama. Fine-tune a pre-trained model using Hugging Face Transformers for text classification or question answering.

Phase 4: MLOps and Production (Weeks 13-16)

Build end-to-end ML pipelines: data versioning (DVC), experiment tracking (MLflow), model registry, containerization with Docker, API serving with FastAPI, monitoring model drift, and CI/CD for ML. Deploy a model as a REST API with automated retraining.

Common Mistakes

1. Jumping to deep learning without mastering linear regression and model evaluation fundamentals
2. Using complex models when simple baselines outperform them — always start with a baseline
3. Data leakage — using future information or test data during training
4. Ignoring class imbalance and evaluating only on accuracy
5. Not using version control for data, models, or experiments
6. Deploying models without monitoring for data drift or concept drift
7. Overfitting on the training set and failing to generalize to unseen data
8. Spending months on model improvement and zero time on deployment infrastructure

Progress Checklist

Learning Resources

• StatQuest with Josh Starmer — Statistics and ML concepts explained visually
• Fast.ai Practical Deep Learning — Top-down approach teaching you to build models first
• CS229 (Stanford / Andrew Ng) — Theoretical foundations of machine learning
• Deep Learning Specialization (deeplearning.ai) — Comprehensive deep learning from basics to advanced
• Hugging Face Course — Learn Transformers, NLP, and model deployment
• Full Stack Deep Learning — MLOps and production ML engineering

Next Steps

After completing this path, explore specialized areas: Reinforcement Learning for game AI and robotics, Generative AI for image and music generation, or MLOps for production infrastructure. Contribute to open source ML projects and build a portfolio of deployed models.

Built by the developers of Doda Browser, DodaZIP, and Durga Antivirus Pro.