Machine Learning
Master's Program
About Courses
All course - 6 months
Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.
As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.
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STEP 1
Introduction
Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
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STEP 2
Linear Regression with One Variable
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
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STEP 3
Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.
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STEP 4
Regularization
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
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STEP 5
Neural Networks: Representation
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
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STEP 6
Advice for Applying Machine Learning
Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
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STEP 7
Unsupervised Learning
We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
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STEP 8
Large Scale Machine Learning
Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
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STEP 9
Application Example: Photo OCR
Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.
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You'll get:
Machine Learning
- Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
- Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
- Choose suitable models for different applications
- Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Course teachers
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Albert Einstein
Founder & CEO -
Nelson Joseph
Co-founder & CTO -
Merlin Shakila
Technical Executive, Software -
Ashwin Daniel
Business Development Executive
Registration
The intensive course is now open, You will receive detailed information on admission by mail immediately after registration
Price: ₹50,000 + GST
our consultant will contact you to clarify further details