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Machine Learning Roadmap | SlaveCode
Machine Learning
Step by step guide to becoming a Machine Learning Engineer in 2026
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Roadmap
Vedios and Other Resources
Projects
AI Tutor
Machine Learning
Pre-requisites
Programming Fundamentals
Mathematical Foundations
Data Collection
Data Cleaning
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Model Evaluation
Deep Learning
Advanced Concepts in ML
Also visit the following related roadmaps
Python Roadmap
AI & Data Scientist
MLOps
AI Engineer
AI Agents
Related Roadmaps
AI Engineer Roadmap
MLOps Roadmap
AI and Data Scientist Roadmap
Introduction
ML Engineer vs AI Engineer
Skills and Responsibilities
What is an ML Engineer?
Linear Algebra
Calculus
Chain rule of derivation
Gradient, Jacobian, Hessian
Derivatives, Partial Derivatives
Scalars, Vectors, Tensors
Singular Value Decomposition
Matrix & Matrix Operations
Eigenvalues, Diagonalization
Determinants, inverse of Matrix
Statistics
Basics of Probability
Descriptive Statistics
Basic concepts
Types of Distribution
Random Variances, PDFs
Bayes Theorem
Inferential Statistics
Graphs & Charts
Probability
Discrete Mathematics
Python
Basic Syntax
Variables and Data Types
Conditionals
Data Structures
Exceptions
Functions, Builtin Functions
Loops
Object Oriented Programming
Essential libraries
Numpy
Pandas
Matplotlib
Seaborn
Data Sources
Databases (SQL, No-SQL)
Internet
APIs
Mobile Apps
IoT
Data Formats
JSON
Parquet
CSV
Excel
Other Data Formats
Preprocessing Techniques
Data Cleaning
Dimensionality Reduction
Feature Engineering
Feature Selection
Feature Scaling & Normalization
Types of Machine Learning
Unsupervised Learning
Semi-supervised Learning
Supervised Learning
Reinforcement Learning
Self-supervised Learning
What is Machine Learning?
What is Supervised Learning?
Classification
Logistic Regression
Support Vector Machines
K-Nearest Neighbors (KNN)
Gradient Boosting Machines
Decision Trees, Random Forest
Regression
Linear Regression
Polynomial Regression
What is Unsupervised Learning?
Clustering
Dimensionality Reduction
Overlapping
Hierarchical
Exclusive
Probabilistic
Autoencoders
Principal Component Analysis
What is Reinforcement Learning?
Policy Gradient
Actor-Critic Methods
Deep-Q Networks
Q-Learning
What is Model Evaluation?
Metrics to Evaluate
Accuracy
Precision
Recall
F1-Score
ROC-AUC
Log Loss
Confusion Matrix
Forward propagation
Back Propagation
Perceptron, Multi-layer Perceptrons
Activation Functions
Neural Network (NN) Basics
Loss Functions
Scikit-learn
Ridge
Lasso
Validation Techniques
LOOCV
K-Fold Cross Validation
Deep Learning Architectures
Convolutional Neural Network
Pooling
Padding
Convolution
Strides
Applications of CNNs
Image Classification
Image Segmentation
Image & Video Recognition
Recommendation Systems
Recurrent Neural Networks
RNN
GRU
LSTM
Attention Mechanisms
Autoencoders
Transformers
Multi-head Attention
Self-Attention
Generative Adversarial Networks
Natural Language Processing
Tokenization
Lemmatization
Stemming
Embeddings
Attention Models
Explainable AI
Train - Test Data
Data Preparation
Data Loading
Tuning
Prediction
Model Selection
Deep Learning Libraries
TensorFlow
Keras
PyTorch
Scikit-learn
ElasticNet Regularization
Why is it important?