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This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.

shantanu1109/Coursera-DeepLearning.AI-Stanford-University-Machine-Learning-Specialization

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DeepLearning.AI, Stanford University - Machine Learning Specialization

This Repository Contains Solution to the Assignments of the Machine Learning Specialization from deeplearning.ai on Coursera Taught by Andrew Ng , Eddy Shyu , Aarti Bagul , Geoff Ladwig

WHAT YOU WILL 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

About this Specialization

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course 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.

It 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), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Applied Learning Project

By the end of this Specialization, you will be ready 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.

There are 3 Courses in this Specialization

Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will:

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
Advanced Learning Algorithms

In the second course of the Machine Learning Specialization, you will:

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
Unsupervised Learning, Recommenders, Reinforcement Learning

In the third course of the Machine Learning Specialization, you will:

Certificate

Machine Learning Specialization (Final Certificate)

📝 Disclaimer

I made this repository as a reference . Please do not copy paste the solution as is. You can find the solution if you read the instruction carefully.

The gem is available as open source under the terms of the MIT License .

Code of conduct

Jupyter Notebook 94.6%
Python 5.4%

Assignments

Jump to: [Homeworks] [Projects] [Quizzes] [Exams]

There will be one homework (HW) for each topical unit of the course. Due about a week after we finish that unit.

These are intended to build your conceptual analysis skills plus your implementation skills in Python.

HW0 : Numerical Programming Fundamentals
HW1 : Regression, Cross-Validation, and Regularization
HW2 : Evaluating Binary Classifiers and Implementing Logistic Regression
HW3 : Neural Networks and Stochastic Gradient Descent
HW4 : Trees
HW5 : Kernel Methods and PCA

After completing each unit, there will be a 20 minute quiz (taken online via gradescope).

Each quiz will be designed to assess your conceptual understanding about each unit.

Probably 10 questions. Most questions will be true/false or multiple choice, with perhaps 1-3 short answer questions.

You can view the conceptual questions in each unit's in-class demos/labs and homework as good practice for the corresponding quiz.

There will be three larger "projects" throughout the semester:

Project A: Classifying Images with Feature Transformations
Project B: Classifying Sentiment from Text Reviews
Project C: Recommendation Systems for Movies

Projects are meant to be open-ended and encourage creativity. They are meant to be case studies of applications of the ML concepts from class to three "real world" use cases: image classification, text classification, and recommendations of movies to users.

Each project will due approximately 4 weeks after being handed out. Start early! Do not wait until the last few days.

Projects will generally be centered around a particular methodology for solving a specific task and involve significant programming (with some combination of developing core methods from scratch or using existing libraries). You will need to consider some conceptual issues, write a program to solve the task, and evaluate your program through experiments to compare the performance of different algorithms and methods.

Your main deliverable will be a short report (2-4 pages), describing your approach and providing several figures/tables to explain your results to the reader.

You’ll be assessed on effort, the sophistication of your technical approach, the clarity of your explanations, the evidence that you present to support your evaluative claims, and the performance of your implementation. A high-performing approach with little explanation will receive little credit, while a careful set of experiments that illuminate why a particular direction turned out to be a dead end may receive close to full credit.

Data Science
Data Analysis
Data Visualization
Machine Learning
Deep Learning
Computer Vision
Artificial Intelligence
AI ML DS Interview Series
AI ML DS Projects series
Data Engineering
Web Scrapping

100+ Machine Learning Projects with Source Code [2024]

Machine Learning gained a lot of popularity and become a necessary tool for research purposes as well as for Business. It is a revolutionary field that helps us to make better decisions and automate tasks. Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

In this article, you’ll find the top 100+ latest Machine Learning projects and Ideas which are beneficial for both beginners and as well experienced professionals. Whether you’re a final-year student aiming for a standout resume or someone building a career, these machine learning projects provide hands-on experience, launching you into the exciting world of Machine Learning and Data Science .

Top Machine Learning Project with Source Code [2024]

We mainly include projects that solve real-world problems to demonstrate how machine learning solves these real-world problems like: – Online Payment Fraud Detection using Machine Learning in Python, Rainfall Prediction using Machine Learning in Python, and Facemask Detection using TensorFlow in Python.

Machine Learning Project for Beginners
Advanced Machine Learning Projects

These projects provide a great opportunity for developers to apply their knowledge of machine learning and make an application that benefits society. By implementing these projects in data science, you be familiar with a practical working environment where you follow instructions in real time.

Machine Learning Project for Beginners in 2024 [Source Code]

Let’s look at some of the best new machine-learning projects for beginners in this section and each project deals with a different set of issues, including supervised and unsupervised learning, classification, regression, and clustering. Beginners will be better prepared to tackle more challenging tasks by the time they have finished reading this article and have a better understanding of the fundamentals of machine learning.

2. Finance and Economics

3. food and beverage, 4. retail and commerce, 6. health and fitness, 7. transportation and traffic, 8. environmental science, 9. text and image processing, 10. social media and sentiment analysis, 11. other important machine learning projects, advanced machine learning projects with source code [2024].

We have discussed a variety of complex machine-learning ideas in this section that are intended to be challenging for users and span a wide range of topics. These subjects involve creating deep learning models, dealing with unstructured data, and instructing sophisticated models like convolutional neural networks, gated recurrent units, large language models, and reinforcement learning models.

1. Image and Video Processing

2. recommendation systems, 3. speech and language processing, 4. health and medical applications, 5. security and surveillance, 6. gaming and entertainment, 7. other advanced machine learning projects, machine learning projects – faqs, what are some good machine-learning projects.

For beginners, recommended machine learning projects include sentiment analysis, sales forecast prediction, and image recognition.

How do I start an ML project?

To start a machine learning project, the first steps involve collecting data, preprocessing it, constructing data models, and then training those models with that data.