Free Deep Learning Course with Certification

This deep learning course is designed to help learners build a strong foundation in one of the most impactful fields of artificial intelligence. Whether you're a student, software engineer, data analyst, or aspiring AI professional, the course provides a structured introduction to deep learning concepts and their real-world applications. The curriculum covers essential topics such as neural networks, backpropagation, activation functions, optimization algorithms, regularization techniques, and deep multi-layer perceptrons. Learners also gain hands-on experience with industry-standard frameworks like TensorFlow and Keras, enabling them to build, train, and evaluate machine learning models effectively. A key focus of the course is helping students understand the fundamentals of deep learning, including how modern AI systems learn from data and make predictions. Through practical examples and guided exercises, learners develop the skills required to solve complex problems using deep learning techniques. The course also introduces concepts used in computer vision, natural language processing, and predictive analytics, preparing learners for more advanced AI studies. With self-paced lessons, expert instruction, and a certificate upon completion, this program is an excellent starting point for anyone looking to explore and apply deep learning in real-world scenarios.

About Course

Deep learning is the engine behind most of today's advanced AI systems, from image recognition to language models, and this deep learning course is built to take you from the underlying theory to actually building models yourself. Led by Srikanth Varma, Lead DSML Instructor at Scaler, the course runs 13 hours and 6 minutes across 3 modules and 3 hands-on challenges, giving you a genuinely thorough grounding in about deep learning rather than a surface-level overview.

The course starts with the fundamentals of deep learning, how biological neurons inspired artificial neural networks, how a single-neuron model and multi-layer perceptron (MLP) are trained, and how backpropagation and activation functions actually work under the hood, before addressing practical issues like the vanishing gradient problem and the bias-variance tradeoff. From there, it goes deeper into Deep MLPs, covering dropout and regularization, weight initialization, batch normalization, and a full walkthrough of optimizers from SGD and momentum to AdaGrad, RMSProp, and Adam, the kind of neural network course content that explains not just what to use, but when and why.

The final module puts all of this into practice with TensorFlow and Keras, where you'll work in Google Colab to build and compare models on the MNIST dataset, testing sigmoid versus ReLU activations, batch normalization, dropout, and hyperparameter tuning, which is where the course turns into real deep learning projects rather than isolated exercises. Once you complete all three modules and challenges, you'll earn a Scaler Certificate of Excellence as your deep learning certification, backed by an instructor with 13 years of AI/ML experience across Amazon, Yahoo! Labs, and Applied AI.

Pre-requisites for free deep learning certification course

1. Basic Programming Knowledge: A foundational understanding of any programming language is necessary, with Python being the most commonly used language in machine learning and deep learning.
2. Understanding of Machine Learning: A basic grasp of machine learning concepts and principles will be beneficial, as deep learning is a subset of machine learning and forms the fundamentals of deep learning.
3. Mathematics: Knowledge of basic mathematics, especially in areas like linear algebra, calculus, and statistics, is useful, as these concepts often underpin the mechanisms of deep learning algorithms.
4. Eagerness to Learn: Deep learning is a complex field. A strong willingness to learn, along with the readiness to invest time and effort, is a crucial prerequisite for this deep learning course.

Who should learn this free deep learning course?

1. Aspiring AI and ML Professionals: Individuals looking to enter the fields of artificial intelligence and machine learning would find this deep learning course highly valuable as deep learning is a key subset of these areas.
2. Software Engineers: Software engineers aiming to broaden their skill set to include AI and machine learning capabilities can benefit significantly from this course.
3. Data Scientists and Analysts: Professionals in these roles who wish to incorporate deep learning techniques into their data analysis and predictive modeling work should consider this course.
4. Researchers and Academics: Scholars and researchers in fields like cognitive science, computer science, and artificial intelligence can use this course to deepen their understanding about deep learning for cutting-edge research and development.