Important Machine Learning Algorithms 👆

Essential statistics topics for data science

1. Denoscriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.

2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.

3. Probability theory: Concepts of probability, random variables, and probability distributions.

4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.

5. Statistical modeling: Linear regression, logistic regression, and time series analysis.

6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.

7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.

8. Data visualization: Techniques for visualizing data and communicating insights effectively.

9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.

10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

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Mathematics for Data Science Roadmap

Mathematics is the backbone of data science, machine learning, and AI. This roadmap covers essential topics in a structured way.


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1. Prerequisites

✔ Basic Arithmetic (Addition, Multiplication, etc.)
✔ Order of Operations (BODMAS/PEMDAS)
✔ Basic Algebra (Equations, Inequalities)
✔ Logical Reasoning (AND, OR, XOR, etc.)


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2. Linear Algebra (For ML & Deep Learning)

🔹 Vectors & Matrices (Dot Product, Transpose, Inverse)
🔹 Linear Transformations (Eigenvalues, Eigenvectors, Determinants)
🔹 Applications: PCA, SVD, Neural Networks

📌 Resources: "Linear Algebra Done Right" – Axler, 3Blue1Brown Videos


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3. Probability & Statistics (For Data Analysis & ML)

🔹 Probability: Bayes’ Theorem, Distributions (Normal, Poisson)
🔹 Statistics: Mean, Variance, Hypothesis Testing, Regression
🔹 Applications: A/B Testing, Feature Selection

📌 Resources: "Think Stats" – Allen Downey, MIT OCW


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4. Calculus (For Optimization & Deep Learning)

🔹 Differentiation: Chain Rule, Partial Derivatives
🔹 Integration: Definite & Indefinite Integrals
🔹 Vector Calculus: Gradients, Jacobian, Hessian
🔹 Applications: Gradient Descent, Backpropagation

📌 Resources: "Calculus" – James Stewart, Stanford ML Course


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5. Discrete Mathematics (For Algorithms & Graphs)

🔹 Combinatorics: Permutations, Combinations
🔹 Graph Theory: Adjacency Matrices, Dijkstra’s Algorithm
🔹 Set Theory & Logic: Boolean Algebra, Induction

📌 Resources: "Discrete Mathematics and Its Applications" – Rosen


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6. Optimization (For Model Training & Tuning)

🔹 Gradient Descent & Variants (SGD, Adam, RMSProp)
🔹 Convex Optimization
🔹 Lagrange Multipliers

📌 Resources: "Convex Optimization" – Stephen Boyd


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7. Information Theory (For Feature Engineering & Model Compression)

🔹 Entropy & Information Gain (Decision Trees)
🔹 Kullback-Leibler Divergence (Distribution Comparison)
🔹 Shannon’s Theorem (Data Compression)

📌 Resources: "Elements of Information Theory" – Cover & Thomas


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8. Advanced Topics (For AI & Reinforcement Learning)

🔹 Fourier Transforms (Signal Processing, NLP)
🔹 Markov Decision Processes (MDPs) (Reinforcement Learning)
🔹 Bayesian Statistics & Probabilistic Graphical Models

📌 Resources: "Pattern Recognition and Machine Learning" – Bishop


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Learning Path

🔰 Beginner:

✅ Focus on Probability, Statistics, and Linear Algebra
✅ Learn NumPy, Pandas, Matplotlib

⚡ Intermediate:

✅ Study Calculus & Optimization
✅ Apply concepts in ML (Scikit-learn, TensorFlow, PyTorch)

🚀 Advanced:

✅ Explore Discrete Math, Information Theory, and AI models
✅ Work on Deep Learning & Reinforcement Learning projects

💡 Tip: Solve problems on Kaggle, Leetcode, Project Euler and watch 3Blue1Brown, MIT OCW videos.