A 10-part series designed to introduce newcomers to machine learning concepts, workflows, tools, and practical implementation — with no prior experience required.
An overview of what machine learning is, its importance in today's world, and how it differs from traditional programming.
Exploring supervised learning, unsupervised learning, and reinforcement learning along with examples to illustrate each type.
Understanding the typical steps involved in a machine learning project from data collection to model deployment.
An introduction to Python programming language and its popular libraries like NumPy and Pandas used in machine learning.
Exploring the process of selecting, transforming, and creating features to improve the performance of machine learning models.
Discussing various machine learning algorithms, how to select the right model for a task, and methods to evaluate model performance.
An introduction to artificial neural networks, their architecture, activation functions, and how they can be trained to learn complex patterns.
Exploring the fundamentals of deep learning, deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Introducing the basics of natural language processing, text preprocessing, feature extraction, and common NLP techniques like sentiment analysis.
Guidance on how to approach and implement real-world machine learning projects, including data preparation, model building, and deployment.