*If you’ve landed on this article, chances are that you’ve been wondering what Machine Learning is all about or perhaps how to get started off. Do not worry, let us get all of this covered in the next few minutes!*

Lately, it seems that every time you open your browser or casually scroll across the news feed, there’s always someone writing about machine learning, its impact on human-kind or the advancements in AI. What’s all this buzz about? Have you ever wondered how technologies ranging from *Virtual Assistant Solutions *to *self-driving cars** and *robots *ever function?

*In the next few minutes, we shall get ‘**Pandas’** covered — An extremely popular Python library that comes with **high-level data structures** and a wide range of tools for **data analysis **that every Machine Learning practitioner must be familiar with!*

*“Pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python” — **Pandas’ Mission Statement*

*Fast and efficient**data manipulation**with**integrated indexing**Integrated tools**for reading/writing in various formats — CSV, text files, MS Excel, SQL, HDF5 etc.**Smart**data-alignment**, integrated**handling of missing values**Flexible**in terms of**reshaping/pivoting**datasets**Supports**slicing…*

*In the next few minutes, we shall get Numpy covered! An extremely popular core scientific computing Python library that every Machine Learning practitioner must be familiar with!*

** NumPy** —

It is very useful for fundamental scientific computations in ** Machine Learning**. It is particularly useful for

Numpy employes something called — ‘*Vectorization’.*

** Vectorization** is a powerful ability…

*Basics of Reinforcement Learning with Real-World Analogies and a Tutorial to Train a Self-Driving Cab to pick up and drop off passengers at right destinations using Python from Scratch.*

Most of you must have probably heard of AI learning to play computer games on its own, a very popular example being ** Deepmind**, which

So what’s the secret behind this major breakthrough? Hold your horses! You’ll understand this in a couple of minutes.

Let’s consider the analogy of teaching…

*Crime pattern analysis** uncovers the underlying interactive process between crime events by discovering where, when, and why particular crimes are likely to occur. The outcomes improve our understanding of the dynamics of unlawful activities and can enhance predictive policing.*

For more on K-means Clustering:

Everything you need to know about K-Means Clustering

*Wget the data required at **this link**:*

`!wget https://raw.githubusercontent.com/namanvashistha/doctor_strange/master/crime.csv`

*Import libraries:*

`import pandas as pd`

import numpy as np

from matplotlib import pyplot as plt

*Read and Display data:*

`data = pd.read_csv("crime.csv")`

data

*K-means from Scratch:*

`np.random.seed(42)`

def euclidean_distance(x1, x2):

return np.sqrt(np.sum((x1 - x2)**2))

class KMeans()…

** KNN (K-Nearest Neighbours) **is one of the most basic classification algorithms in machine learning. It belongs to the

The way we measure similarity is by creating a vector representation of the items, and then compare the vectors using an appropriate distance metric *(like the Euclidean distance, for example).*

For more on KNN:

A Beginner’s Guide to KNN and MNIST Handwritten Digits Recognition using KNN from Scratch

*Dataset used:*

We used `haarcascade_frontalface_default.xml`

dataset that could easily be downloaded from *this link**.*

*…*

*While many classifiers exist that can classify linearly separable data such as logistic regression, **Support Vector Machines (SVM)** can handle highly non-linear problems using a **kernel trick** which implicitly maps the input vectors to higher-dimensional feature spaces.*

Let’s get into the depth of this in the next few minutes!

This transformation we were talking about — rearranges the dataset in such a way that it is then *linearly solvable.*

In this article, we are going to look at how SVM works, learn about kernel functions, hyperparameters and pros and cons of SVM along with some of the real-life applications of…

*Naive Bayes** is a probabilistic machine learning algorithm based on the **Bayes Theorem**, used in a wide variety of classification tasks.*

*In this article, we shall be understanding the Naive Bayes algorithm and its essential concepts so that there is no room for doubts in understanding.*

*Naive Bayes is a simple but surprisingly powerful probabilistic machine learning algorithm used for predictive modeling and classification tasks.*

Some typical applications of Naive Bayes are ** spam filtering**,

*In statistics, **Naive Bayes** classifiers are a family of simple **“probabilistic classifiers”** based on applying **Bayes’ theorem** with strong independence assumptions between the features. Source: **Wikipedia*

For the conceptual overview of Naive Bayes, refer —

A Machine Learning Roadmap to Naive Bayes

*We shall now go through the code walkthrough for the implementation of the Naive Bayes algorithm from scratch:*

`import `**numpy** as np

**class NaiveBayes**:

**def fit**(self, X, y):

n_samples, n_features = X.shape

self._classes = np.unique(y)…

*Support Vector Machine** or **SVM **is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.*

*The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.*

*Source: **Javatpoint*

For the conceptual overview of SVM, refer —

A Beginner’s Introduction to SVM

*We shall now…*

CS Undergrad at Bennett University