Name: Foundations of Machine Learning: A Beginner’s Journey
Availability: InStock

What You'll Learn

Understand the core concept of machine learning and how it powers modern AI systems.
Get introduced to neural networks — the building blocks of modern AI, and learn how backpropagation works.
Dive into deep learning and how it powers things like image recognition and voice assistants.
Learn the difference between supervised, unsupervised, and reinforcement learning.
Build models for classification and regression to make predictions from data.
Explore clustering and density estimation for discovering patterns in unlabeled data.
Master data preprocessing techniques like handling missing values, normalization, and feature engineering.
Evaluate models using ROC curves, AUC, and other performance metrics.
Understand how probability helps machines make smarter guesses, including Bayes classifiers and logistic regression.
Apply linear models and optimize them using gradient descent.
Learn about generative models such as GANs and variational autoencoders (VAEs).
Understand how machines deal with sequences, like predicting the next word in a sentence.
Implement ensemble methods like bagging, boosting, and random forests.
Work with sequence models including RNNs and LSTMs for time-series and text.
Build embedding models for recommender systems and graph-based learning.
Understand reinforcement learning concepts like policy gradients and Q-learning.
Reflect on the social and ethical impact of machine learning in real-world applications.

Requirements

Familiarity with high school-level mathematics (algebra, probability, and statistics).
Curiosity and motivation to learn machine learning concepts deeply.

Beginners who want a comprehensive introduction to machine learning.
Students and professionals looking to strengthen their understanding of ML theory and practice.
Data scientists and analysts seeking to expand their toolkit with modern ML techniques.
Software engineers interested in integrating machine learning into applications.
Researchers and academics exploring the social and ethical implications of AI.

A hub for learning modern skills.

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4,001 Students

10 Courses

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