Machine Learning Specialization

Goal: To Advance Your Career | Salary: ₹7-10 lakh | Professional Certificate | Basic Coding & High School Math Experience Needed | Taught by DeepLearning.AI and Stanford Online | Duration: 2 Months

A job-oriented 3-course series that helps you master fundamental AI concepts and develop practical machine learning skills in a beginner-friendly way. The program is the best place to start if you are looking to break into AI or build a career in machine learning.

What You’ll Learn

• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

What You’ll Earn

• ₹7-10 lakh per year is the average starting salary for a Machine Learning professional in India (Source: careers360.com, glassdoor.co.in)
• $112,000+ per year is the average salary for a Machine Learning professional in the US (Source: ziprecruiter.com)

Details

This 3-course beginner-friendly program teaches you the fundamentals of machine learning and use these techniques to build real-world AI applications.

This program is taught by AI visionary Andrew Ng who has led critical research at Stanford University and done ground-breaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

You don’t need any specialised skills to take the program. Understanding basic coding concepts such as loops, functions, if/else statements and high school math concepts in arithmetic and algebra is good enough.

The program provides a broad introduction to modern machine learning, including:

• Supervised Learning: Multiple linear regression, logistic regression, neural networks, and decision trees
• Unsupervised Learning: Clustering, dimensionality reduction, recommender systems
• AI and ML best practices: Evaluating and tuning models, taking a data-centric approach to improving performance

By the end of this program, you will have mastered key concepts and gained the practical knowledge to apply machine learning to challenging real-world problems.

Applied Learning Project

The program will teach you to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method
• Build a deep reinforcement learning model

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