Machine Learning Foundations

Machine Learning is mainly 3 kinds of algorithms - Supervised, Unsupervised and Reinforcement learning. In supervised learning, the training data set has certain input column and one of more output columns. Both the input and output columns ‘supervise’ the training of the model.

The way they ‘supervises’ is by determining the prediction error which is the difference between what the current state of the model outputs vs. what the actual output should be.

This way you try to improve the model in multiple iterations by minimizing the error, and the actual value of the output (and weights assigned to the inputs) ‘supervising’ the direction of the model in each iterations. If there were no output values (like in clustering, which is unsupervised learning) then you will never know how good your model is predicting and you will not be able to supervise it for better accuracy.

While the above approach is how most ML algorithms work, some ML algorithms are closed form in nature (meaning they have a ready made Math formula). Both simple and multi-variate regression are examples for these. If you do regression using the closed form formula you plug in the inputs and outputs at the same time into the equation and out comes the weights. This is still supervised learning as both inputs and outputs determine the final state of your model.

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What is linear regression analysis?

What are the algorithms that a machine learning aspirant must know?

What are some common ML Interview Questions?

Regression is a technique that predicts the value of variable ‘y’ based on the values of variable ‘x’. In simple terms, Regression helps to find the relation between two things.

Linear regression is used to understand the relation between two variables. Linear regression is a type of supervised algorithm, it is sed for finding a linear relationship between the independent and dependent variable and finds the relationship between two or more continuous variable.

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The 3 main types of Machine Learning Algorithms are-

  1. Supervised Learning
    How it works: This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

  2. Unsupervised Learning
    How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.

  3. Reinforcement Learning:
    How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Example of Reinforcement Learning:
Markov Decision Process
List of commonly used machine learning algorithms.
Linear Regression
Logistic Regression
Decision Tree
Naive Bayes
Random Forest
Dimensionality Reduction Algorithms

A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods and clarity of basic concepts. If you aspire to apply for machine learning jobs, it is crucial to know what kind of interview questions generally recruiters and hiring managers may ask. Here are some of them:

  1. What are the different types of Learning/ Training models in ML?
  2. What is the difference between deep learning and machine learning?
  3. How do you select important variables while working on a data set?
  4. How are covariance and correlation different from one another?
  5. When does regularization come into play in Machine Learning?

Prerequisites to start learning ML?

I would say it is mandatory to learn a programming language, preferably Python, along with the required analytical and mathematical knowledge.
Here are some mathematical areas that you need to brush up before jumping into solving Machine Learning problems:

Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors
Mathematical Analysis: Derivatives and Gradients
Probability theory and statistics
Multivariate Calculus
Algorithms and Complex Optimizations

What exactly does a Machine Learning Engineer do?

What is regularization in machine learning?

Machine Learning engineers work on a lot of interdisciplinary tasks including data science, analytics, business communication and much more. Here is what an ML Engineer would do on a day to day basis:

Checking active models
Connecting with their team for updates
Supervising task management platforms for the day
Analysis of company codebase (using Scikit learn) to look for bugs
Coding with PyCharm to implement models
Meeting stakeholders to ensure products are updated with new features
Optimisation of products
Creating plans and processes for products
Researching on the latest trends in the domain to benefit their company

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Regularization is a concept that is used to tackle the problem of over-fitting. I am going to focus on the regularization techniques. Some of the popular regularization techniques are:

Dropout Regularization
L1 Regularization
L2 Regularization
Batch standardization

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Can anybody prescribe me the techniques used in supervised ML other than Linear and polynomial regressions?

Why traceback most recent call last error : Not fitted error is coming while implementing logistic regression prediction function inspite of that I have not used any NaN values ?

From my course structure of Machine learning Foundations Rao sir videos are not visible for me.

Plz help for this query.

From course structure, Kaggle Data set is also not visible for me.

After Watching the lecture of Rao sir, Kaggle dataset and Rao sir lectures are not queue in the course structure.

plz add them.
It will help me

Hello Learner,
Please drop a mail to mentioning the course name.

where can i find answers to assignments of ML course

hey can any body help me to find Boston.csv file because if i use it downloads from online it give error when i train model and fit data in model