📌 Step 1 - Maths.
🔍Linear Algebra:
📌Vectors and matrices
📌Matrix operations (addition, subtraction, multiplication)
📌Eigenvalues and eigenvectors
📌Singular Value Decomposition (SVD)
🔍Calculus:
📌Limits and continuity
📌Derivatives and integrals
📌Partial derivatives
📌Multivariate calculus
Statistics:
📌Descriptive statistics (mean, median, mode)
📌Probability theory
📌Probability distributions (normal, binomial, Poisson)
📌Hypothesis testing and confidence intervals
📌Regression analysis
📌Optimization:
📌Gradient descent
📌Convex optimization
Differential Equations:
📌Ordinary Differential Equations (ODEs)
📌Partial Differential Equations (PDEs)
Discrete Mathematics:
📌Set theory
📌Graph theory
📌Combinatorics
Numerical Methods:
📌Root finding
📌Numerical integration
📌Solving linear systems
Linear Regression:
📌Understanding and implementing linear regression models
Probability and Bayes' Theorem:
📌Understanding basic probability concepts
📌Bayes' theorem and its applications in machine learning
Mathematical Programming:
📌Linear programming
📌Non-linear programmin
📊 Algorithms
🤖 Linear Regression: Used for predicting a continuous variable based on one or more predictor variables.
🤖Logistic Regression: Used for binary classification problems.
🤖Decision Trees: Non-linear model used for both classification and regression tasks.
🤖Random Forest: An ensemble method using multiple decision trees, often performing better than a single tree.
🤖Support Vector Machines (SVM): Used for classification and regression tasks, particularly effective in high-dimensional spaces.
🤖K-Nearest Neighbors (KNN): A simple and intuitive algorithm used for both classification and regression tasks.
🤖K-Means Clustering: Unsupervised learning algorithm for partitioning data into clusters.
🤖Hierarchical Clustering: Another unsupervised learning algorithm for grouping similar data points into clusters.
🤖Principal Component Analysis (PCA): A dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.
🤖Naive Bayes: A probabilistic algorithm commonly used for classification tasks, particularly in natural language processing.
🤖Gradient Boosting Algorithms (e.g., XGBoost, LightGBM): Ensemble methods that build a strong model from multiple weak models in a sequential manner.
🤖Neural Networks: Deep learning models used for complex tasks like image and speech recognition, natural language processing, etc.
🤖Recurrent Neural Networks (RNNs): A type of neural network designed for sequential data.
🤖Long Short-Term Memory Networks (LSTMs): A specialized type of RNN that is particularly effective in learning long-term dependencies in sequential data.
🤖Convolutional Neural Networks (CNNs): Neural networks designed for processing structured grid data, commonly used in image and video analysis.
🤖Association Rule Mining (e.g., Apriori Algorithm): Used for discovering interesting relationships hidden in large datasets.
🤖Time Series Analysis Algorithms (e.g., ARIMA, Exponential Smoothing): Techniques for analyzing and forecasting time series data.
🤖Word Embeddings (e.g., Word2Vec, GloVe): Techniques for representing words as vectors in a continuous vector space, commonly used in natural language processing.
🤖Recommendation Algorithms (e.g., Collaborative Filtering, Content-Based): Used in recommendation systems to suggest items based on user preferences.
🤖Ensemble Learning: Techniques that combine the predictions of multiple models to improve overall performance.
🤖Programming Languages
📌Python
📌R Programming
📌Matlab
📌 Java
