What are prerequisites to start learning machine learning? from Shivani Salavi's blog

To start learning machine learning, it's helpful to have a foundation in certain prerequisite skills and knowledge areas. Here are some key prerequisites to consider:

Mathematics:

Understanding of basic mathematics concepts, including algebra, calculus, probability, and statistics. Linear algebra is particularly important for understanding machine learning algorithms and concepts such as matrix operations, eigenvalues, and eigenvectors.

Programming:

Proficiency in at least one programming language, such as Python, R, or Julia. Python is widely used in the machine learning community and has extensive libraries and frameworks for machine learning and data science, such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.

Data Analysis and Manipulation:

Familiarity with data analysis and manipulation techniques, including data cleaning, preprocessing, visualization, and exploratory data analysis (EDA). Understanding how to work with structured and unstructured data in various formats (e.g., CSV, JSON, SQL databases) is essential.

Machine Learning Concepts:

Basic understanding of machine learning concepts, algorithms, and techniques, including supervised learning, unsupervised learning, reinforcement learning, classification, regression, clustering, dimensionality reduction, and model evaluation metrics. (Machine Learning Training in Pune)

Linear Algebra and Calculus:

Understanding of linear algebra concepts such as vectors, matrices, matrix multiplication, eigenvalues, and eigenvectors. Linear algebra is foundational to many machine learning algorithms, especially those involving matrix operations.

Knowledge of calculus, including differentiation, integration, and optimization techniques. Calculus is important for understanding how machine learning models are trained and optimized using techniques such as gradient descent.

Probability and Statistics:

Understanding of basic probability theory, including probability distributions, random variables, expected values, variance, and standard deviation. Probability theory forms the basis of many machine learning algorithms, especially those involving probabilistic models.

Knowledge of statistical concepts such as hypothesis testing, confidence intervals, correlation, and regression analysis. Statistics is essential for interpreting data, evaluating models, and making informed decisions in machine learning. (Machine Learning Course in Pune)

Data Structures and Algorithms:

Understanding of basic data structures (e.g., lists, arrays, dictionaries, trees, graphs) and algorithms (e.g., sorting, searching, recursion). Knowledge of data structures and algorithms is important for implementing and optimizing machine learning algorithms and data processing pipelines.

While having a strong background in all of these areas is beneficial, it's not necessary to be an expert in each domain before starting to learn machine learning. Many resources, courses, and tutorials are available that cater to different skill levels and learning styles. As you progress in your machine learning journey, you can deepen your understanding of these concepts and techniques through practice, experimentation, and continuous learning.