ahujaraman/machine-learning-coursera-stanford

📊 📈 In depth explained my assignment solutions. Grade: 97.3%

machine-learning-coursera-stanford

My assignment solutions. I sincerely request to first try out on your own and if you get stuck, you can have a look at my explained solutions. Grade: 97.3% Certificate

Assignments

1. Linear Regression

In this part of this exercise, you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next.

• Model Representation and Cost function
• Gradient descent for linear regression
• Multivariate Linear Regression
• Feature Scaling & Learning Rate
• Plotting Data

2. Logistic Regression

In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and you want to determine each applicant's chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant's scores on two exams and the admissions decision. Your task is to build a classification model that estimates an applicant's probability of admission based the scores from those two exams.

• Logistic Regression Model
• Multiclass Classification (One vs all method)
• Regularization (Solving the Problem of Overfitting)

3. Multi-class Classification and Neural Networks

In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize hand-written digits(from 0 to 9)

• Neural Networks Model Representation
• Feedforward Propagation and Prediction

4. Neural Networks Learning

In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.

• Neural Networks Cost Function and Backpropagation
• Concept of Random Initialization

5. Regularized Linear Regression and Bias v.s.Variance

In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties.

• Evaluating a Learning Algorithm
  • Model Selection and Train/Validation/Test sets
• Bias v.s.Variance
• Learning Curves

6. Support Vector Machines

In this exercise, you will be using support vector machines (SVMs) to build an eamil spam classifier.

• Large Margin Classification
• Kernels
• SVM with Gaussian Kernels

7. K-means Clustering and Principal Component Analysis

In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images.

• Clustering and Unsupervised Learning
• K-means Algorithm
• Dimensionality Reduction
  • Principal Components Analysis
  • Data Compression to speed up Learning Algorithms

8. Anomaly Detection and Recommender Systems

In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies.

• Anomaly detection
  • Density Estimation to detect Anomalies
  • Gaussian distribution
  • Estimating parameters for a Gaussian
  • Selecting the threshold, epsilon
• Recommender Systems
  • Collaborative Filtering
  • Low Rank Matrix Factorization

P.S : After this course, you will be aware of behind the scenes working of the above concepts. To solve assignments, you must be knowing the concepts mentioned for each one individually.