Trending February 2024 # Machine Learning With Python: Top 10 Projects For Freshers To Pursue # Suggested March 2024 # Top 11 Popular

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With source program in Python, check these top 10 Machine Learning with Python projects for freshers

Machine learning is same as how it sounds. It is the idea that multiple types of technology, such as computers and tablets, can learn something from programming and other data. It appears to be an abstract idea. However, this type of technology is used by several people each day. Speech identification is a good example of this. Virtual assistants including Siri and Alexa use technology to present messages, answer questions and respond to instructions.

In this tutorial, you will find top 10 machine learning project ideas for freshers, intermediates, and professionals to gain real-world experience of this developing technology in 2023. These machine learning project ideas will assist you in learning all the practicalities that you want to with prevailing in your profession and to make you employable in the business.

1.Movie Recommendations from Movielens Dataset

Many individuals currently use technology to stream TV and film shows. Although choosing the next stream to watch can be complex and time-consuming, recommendations are generally built based on customer habits and history. This is accomplished by machine learning and is a great and simple task for beginners to tackle. Starting developers can learn by writing program utilizing one of the two languages, Python and R, and using data from Movielens Dataset. Movielens has over 6000 people make it currently involves more than 1 million film valuations of 3900 movies.

2.Music Recommendation System ML Project

This is one of the most popular machine learning projects and can be used across multiple domains. You should be very familiar with a recommendation system if you have utilized any E-commerce site or Movie/Music website. In some E-commerce sites such as Amazon, at the time of checkout, the system will recommend elements that can be added to the cart.

3.BigMart Sales Prediction ML Project

As a fresher, you should work on multiple machine learning projects ideas to expand your skillset. Therefore, we have added a project that will learn unsupervised machine learning algorithms to us by utilizing the business dataset of a grocery supermarket store.


This open-source artificial intelligence library is a best place for fresher to enhance their machine learning skills. With TensorFlow, they can use the library to make data flow graphs, projects utilizing Java, and an array of applications. It also involves APIs for Java.

5.Iris Classification

This is one of the simplest machine learning projects with Iris Flowers being the elementary machine learning datasets in classification writing. This machine learning problem is defined as the “Hello World” of machine learning. The dataset has numeric characteristics and ML freshers need to figure out how to load and handle information. The iris dataset is small which simply fits into the memory and does not need any specific transformations or scaling, to start with.

6.Sales Forecasting with Walmart

While predicting future sales efficiently may not be applicable, businesses can come near to machine learning. For example, Walmart supports datasets for 98 products across 45 outlets so programmer can access data on weekly sales by locations and branch. The main objective of this project is to create better data-driven decisions in channel optimization and stock planning. 

7.Stock Price Predictions

It is same as sales forecasting, forecasts of prices for stocks can be changed from the data of previous prices, indexes of volatility, and different fundamental indicators. For freshers, it is possible to start with a concept like this and create use of stock industry data to create predictions over the recent months. It is a best way to get familiar with making predictions utilizing huge data sets.

8.Breast Cancer Prediction

This project uses machine learning to make data that helps decide whether the tumour in the breast is mild or deadly. There are multiple factors considered, including the thickness of the lump, the number of bare nuclei, and mitosis. It is also a best method for a new expert in machine learning to get familiar with using R.

9.Sorting of Specific Tweets on Twitter

In an optimal world, quickly filtering tweets with definite words and elements would be best. There’s a huge fresher-level machine-learning project which enables programmers to develop an algorithm that takes scraped tweets processed by an artificial language processor to recognize which tweets are more likely to be associated to specific topics or talk about specific individuals, etc.

10.Making Handwritten Documents Digital Versions

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Top 10 Essential Prerequisites For Deep Learning Projects

The deep learning projects are used across industries ranging from medical to e-commerce

Deep learning is clearly the technology of the future and is one of the most sought-after innovations of our day. You should be aware of the requirements for DL if you’re interested in learning it. You can choose a better job path with the aid of deep learning projects prerequisite.

Deep learning is an interdisciplinary area of computer science and mathematics with the goal of teaching to carry out cognitive tasks in a manner that is similar to that of humans. Prerequisites for deep learning projects are a process through which computers collect input data, and study or analyze it. Different methods are used by deep learning prerequisites systems to automatically identify patterns in datasets that may contain structured data, quantitative data, textual data, visual data, etc. We’ll talk about the top requirements for deep learning projects in this section to help you prepare for learning its more complex ideas.

1. Programming

Deep learning requires programming as a core component. Deep learning demands the use of a programming language. Python or R are the programming languages of choice for deep learning experts due to their functionality and efficiency. You must study programming and become proficient in one of these two well-known programming languages before you can study the numerous deep learning topics.

2. Statistics

The study of utilizing data and its visualization is known as statistics. It aids in extracting information from your raw data. Data science and the related sciences depend heavily on statistics. You would need to apply statistics to acquire insights from data as a deep learning specialist.

3. Calculus

The foundation of many machine learning algorithms is calculus. Therefore, studying calculus is a requirement for deep learning. You will create models using deep learning based on the features found in your data. You can use such properties and create the model as necessary with the aid of calculus.

4. Linear Algebra

Linear algebra is most likely one of the most crucial requirements for deep learning. Matrix, vector, and linear equations are all topics covered by linear algebra. It focuses on how linear equations are represented in vector spaces. You may design many models (classification, regression, etc.) with the aid of linear algebra, which is also a fundamental building block for many deep-learning ideas.

5. Probability

Mathematics’ field of probability focuses on using numerical data to express how likely or valid an occurrence is to occur. Any event’s probability can range from 0 to 1, with 0 denoting impossibility and 1 denoting complete certainty.

6. Data Science

Data analysis and use are the focus of the field of data science. You must be knowledgeable with a variety of data science principles to construct models that manage data as a deep learning specialist. Understanding deep learning will assist you in using data to achieve the desired results, but mastering data science is a prerequisite for applying deep learning.

7. Work on Projects

While mastering these topics will aid in the development of a solid foundation, you will also need to work on deep learning projects to ensure that you fully comprehend everything. You can apply what you’ve learned and identify your weak areas with the aid of projects. You can easily find a project that interests you because deep learning has applications in many different fields.

8. Neural Networks

The word “neuron,” which is used to describe a single nerve cell, is where the word “neural” originates. That’s correct; a neural network is essentially a network of neurons that carry out routine tasks for us.

A significant portion of the issues we encounter daily is related to pattern recognition, object detection, and intelligence. The reality is that these reactions are challenging to automate even if they are carried out with such simplicity that we don’t even notice it.

9. Clustering Algorithms

The clustering problem is resolved with the most straightforward unsupervised learning approach. The K-means method divides n observations into k clusters, with each observation belonging to the cluster represented by the nearest mean.

10. Regression

Top 10 Deep Learning Projects For Engineering Students In 2023

If you are one of them wanting to start a career in deep learning, then you must read these top deep 10 learning projects

Deep learning

is a domain with diverse technologies such as tablets and computers that can learn based on programming and other data. Deep learning is emerging as a futuristic concept that can meet the requirements of people. When we take a look at the speech recognition technology and virtual assistants, they are run using

machine learning


deep learning technologies

. If you are one of them wanting to start a career in deep learning, then you must read this article as this article features current ideas for your upcoming deep learning project. Here is the list of the top 10 deep learning projects to know about in 2023.


Due to their skillful handling of a profusion of customer queries and messages without any issue, Chatbots play a significant role for industries. They are designed to lessen the customer service workload by automating the hefty part of the process. Nonetheless, chatbots execute this by utilizing their promising methods supported by technologies like machine learning, artificial intelligence, and deep learning. Therefore, creating a chatbot for your final deep learning project will be a great idea.

Forest Fire Prediction

Creating a forest fire prediction system is one of the best deep learning projects and it will be another considerable utilization of the abilities provided by deep learning. Forest fire is an uncontrolled fire in a forest causing a hefty amount of damage to not only nature but the animal habitat, and human property as well. To control the chaotic nature of forest fires and even predict them, you can create a deep learning project utilizing k-means massing to comprehend major fire hotspots and their intensity.

Digit Recognition System

This project involves developing a digit recognition system that can classify digits based on the set tenets. The project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive.

Image Caption Generator Project in Python

This is one of the most interesting deep learning projects. It is easy for humans to describe what is in an image but for computers, an image is just a bunch of numbers that represent the color value of each pixel. This project utilizes deep learning methods where you implement a convolutional neural network (CNN) with a Recurrent Neural Network (LSTM) to build the image caption generator.

Traffic Signs Recognition

Traffic signs and rules are crucial that every driver must obey to prevent accidents. To follow the rule, one must first understand what the traffic sign looks like. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. For a final-year engineering student, it is one of the best deep learning projects to try.

Credit Card Fraud Detection

With the increase in online transactions, credit card frauds have also increased. Banks are trying to handle this issue using deep learning techniques. In this deep learning project, you can use python to create a classification problem to detect credit card fraud by analyzing the previously available data. 

Customer Segmentation

This is one of the most popular deep learning projects every student should try. Before running any campaign companies create different groups of customers. Customer segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base.

Movie Recommendation System

In this deep learning project, you have to utilize R to perform a movie recommendation through technologies like Machine Learning and

Artificial Intelligence

. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Visual tracking system

A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.

Drowsiness detection system

Top 10 Unsupervized Machine Learning Models To Learn In 2023

Learn about the unsupervised Machine Learning models that are in the topmost position in 2023

Unsupervized Machine Learning models are not supervised using training datasets when using the machine learning technique this is called unsupervised learning. Instead, models themselves decipher the provided data to reveal hidden patterns and insights. It is comparable to the learning process that occurs in the human brain when learning something new.

It primarily deals with unlabelled data. It can be compared to learning, which happens when a learner resolves a problem without the guidance of a teacher. Unsupervised learning cannot be used to solve a regression or classification issue directly. We lack the input data with the corresponding output label, much like supervised machine learning. It aims to identify the underlying pattern of the dataset, group the data based on similarities, and express the dataset in a precise manner.

To understand more about it. Let us know the top 10 Unsupervized Machine Learning Models/algorithms, 

Gaussian mixture models –   It is a probabilistic model that assumes that all of the data points were produced by combining a limited number of Gaussian distributions with unknowable parameters.                         

Frequent pattern growth – Models use algorithms that allow the detection of recurring patterns without candidate production. Instead of employing Apriori’s generate and test strategy, it constructs an FP Tree.

K-means Clustering – This Unsupervised learning is used in the K-Means Clustering technique. It clusters the unlabelled dataset into several groups. The program repeatedly divides the unlabelled dataset into K clusters. Each dataset only belongs to one group that shares common characteristics. It enables us to group the data into different categories. It is a useful technique for finding the groups’ categories in the provided dataset without training.

Hierarchical Clustering – Hierarchical cluster analysis is another name for hierarchical clustering. It is an algorithm for unsupervised clustering. It entails creating clusters that are arranged initially from top to bottom. 

Anomaly Detection – Anomaly detection is most helpful in training scenarios where we have a variety of regular data instances. By allowing the machine to get close to the underlying population, a clear model of normality is produced.

Principal Component Analysis – By utilizing orthogonal transformation, a statistical method converts the observations of correlated characteristics into a group of linearly uncorrelated components. The Principal Components are these newly altered features that make it one of the most widely used machine learning algorithms.

Apriori Algorithm – It utilizes databases that store transactional data. The association rule establishes the strength of the relationship between two objects. The associations for the itemset are chosen using a breadth-first search in this approach. It assists in identifying common item sets in a huge dataset.

KNN (k-nearest neighbors) – A new data point is classified using the K-NN algorithm based on similarity after all the existing data has been stored. This indicates that new data can be easily viewed when it appears.

Neural Networks – Since a neural network approximates any function, it is theoretically conceivable to use one to learn any function.

Independent Component Analysis – This technique works by assuming non-Gaussian signal distribution and enables the separation of a mixture of signals into their various sources.

Conclusion –The biggest drawback of unsupervised learning is that you cannot get precise information regarding data sorting. However, this learning helps you find all kinds of unknown patterns in data. Algorithms used models are important to learn as they are unsupervised and needed to be understood 

Top Machine Learning Jobs To Apply For In December 2023

Analytics Insight announces the top machine learning jobs to apply for in December 2023

The emergence of major disruptive technologies like artificial intelligence, big data, computer vision, and many more have introduced some lucrative machine learning jobs for aspiring machine learning engineers, machine learning specialists, and more. Almost every industry today relies on technology to operate and thrive, which is why artificial intelligence (AI) and machine learning (ML) is becoming more integral to helping businesses make smarter and faster decisions and products. Many companies have started posting machine learning jobs in December to apply as soon as possible. There is tough competition in the recruitment process due to its high demand. Thus, let’s go through some of the top machine learning jobs to apply for in December 2023 with Analytics Insight.

Machine Learning Specialist – Samsung Electronics

Location: Noida, Uttar Pradesh


Machine Learning Lead – Skit

Location: Bangalore Urban, Karnataka


You will be working with Product Managers and ML Engineers to design and deliver ML models and capabilities for our voice-bot offering, VIVA. A regular roster for the role looks like the following:

Formulate, design, and oversee mMachine lLearning capabilities of our speech-tech stack.

Lead a team of ML Engineers in the process of development.

Organize regular research and architecture reviews and discussions.

Mentor other machine lLearning team members.

Manager-Product Development (Machine Learning) – American Express

Location: Gurgaon, Haryana


The individual in this role will be responsible for overlaying sales and marketing strategy needs with analytics and machine learning workstreams and leveraging technology expertise to architect and develop enterprise big data platforms and applications. This individual will work closely with partners across teams like commercial sales and marketing, analytics and machine learning, American Express Technology, external vendors, and others. It is one of the best machine learning jobs to apply for in December 2023.

Machine Learning Engineer – Wipro

Location: India (Remote)


Machine Learning Engineer – Adobe

Location: Bengaluru, Karnataka


The position involves working closely with Product Management for the design, development, debugging, effort estimation, and maintenance of Statistical and Machine Learning models that power various features in AdCloud. Work closely with AdCloud engineering team in building cCloud nNative pipelines taking the sStatistical and ML models through the entire lifecycle improving on usability, explain-ability, and performance.

Machine Learning Engineer II – Amazon

Location: Bengaluru, Karnataka


As a Machine Learning Engineer, you will help solve a variety of technical challenges and mentor other engineers. You will play an active role in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team. You will help invent new features, develop and deploy highly scalable and reliable distributed services. You will work with a variety of core languages and technologies including, Linux, and AWS technologies. It is one of the best machine learning jobs to apply for in December 2023.

Machine Learning Engineer –

Location: India (Remote)


Work on end-to-end aspects of machine learning solutions for the financial domain: acquiring data, training and building models, deploying models, building API services for exposing these models, maintaining them in production.

AI & ML Lead Software Engineer – Parallel Wireless

Location: Bengaluru, Karnataka


Interesting Python Projects With Code For Beginners – Part 2

1. Convert the image to Gray using cv2.COLOR_BGR2GRAY.

cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)

2. Finding contours in the image:

To find contours use cv2.findContours().  It takes three parameters: the source image, contour retrieval mode, contour approximation method. This will return a python list of all contours. Contour is nothing but a NumPy array of (x,y) coordinates of boundary points in the object.

3. Apply OCR.

By looping through each contour, take x,y and width, height using cv2.boundingRect() function. Then draw a rectangle function in image using cv2.rectange(). This has five parameters: input image, (x, y), (x+w, y+h), boundary colour for rectangle, size of the boundary.

4. Crop the rectangular region and pass that to tesseract to extract text. Save your content in a file by opening it in append mode.


import cv2 import pytesseract # path to Tesseract-OCR in your computer pytesseract.pytesseract.tesseract_cmd = 'path_to_tesseract.exe' img = cv2.imread("input.png") #input image gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Converting image to gray scale # performing OTSU threshold # give structure shape and kernel size # kernel size increases or decreases the area of the rectangle to be detected. rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 18)) #dilation on the threshold image dilation = cv2.dilate(img_thresh , rect_kernel, iterations = 1) img_contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) im2 = img.copy() file = open("Output.txt", "w+") #text file to save results file.write("") file.close() #loop through each contour for contour in img_contours: x, y, w, h = cv2.boundingRect(contour) rect = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 2) cropped_image = im2[y:y + h, x:x + w] #crop the text block file = open("Output.txt", "a") text = pytesseract.image_to_string(cropped_image) #applying OCR file.write(text) file.write("n") file.close()

Input image:

Output image:

2. Convert your PDF File to Audio Speech

Say you have some book as PDF to read, but you are feeling too lazy to scroll; how good it would be then if that PDF is converted to an audiobook. So, let’s implement this using python.

We will need these two packages:

pyttsx3: It is for Text to Speech, and it will help the machine speak.

PyPDF2: It is a PDF toolkit. It is capable of extracting document information, merging documents, etc.

Install them using these commands:

pip install pyttsx3 pip install PyPDF2


Import the required modules.

Use PdfFileReader() to read PDF file.

getPage() method is used to select the page to be read from.

Extract the text using extract text().

By using pyttx3, speak out the text.


# import the modules import PyPDF2 import pyttsx3 # path of your PDF file path = open('Book.pdf', 'rb') # PdfFileReader object pdfReaderObj = PyPDF2.PdfFileReader(path) # the page with which you want to start from_page = pdfReaderObj.getPage(12) content = from_page.extractText() # reading the text speak = pyttsx3.init() speak.say(content) speak.runAndWait()

That’s it! It will do the job. This small code is beneficial to you when you don’t want to read; you can hear.

Next, you can provide a GUI to this project using tikinter or anything else. You can give a GUI to enter the pdf path, the page number to start from, a stop button. Try this!

Let’s move to the next project.

3. Reading mails and downloading attachments from the mailbox

Let’s understand what the benefit of reading the mailbox with Python is. So, let’s suppose if we are working on a project where some data comes daily in word or excel, which is required for the script as input or to Machine learning model as input. So, if you have to download this data file daily and give it to the hand, it will be hectic. But if we can automate this step, read this file, and download the required attachment, it would be a great help. So, let’s implement this.

We will use pywin32 to implement automatic attachment download from a particular mail. It can access Windows applications like Excel, PowerPoint, Word, Outlook, etc., to perform some actions. We will focus on Outlook and download attachments from the outlook mailbox.

Note: This does not need authentication like user email id or password. It can access Outlook that is already logged in to your machine. (Keep the outlook app open while running the script).

In the above example, we chose smtplib because it can only send emails and not download attachments. So, we will go with pywin32 to download attachments from Outlook, and it will be pretty straightforward. Let’s look at the code.

Command to install: pip install pywin32

Import module

import win32com.client

Now, establish a connection to Outlook.

outlook = win32com.client.Dispatch(“Outlook.Application”).GetNamespace(“MAPI”)

Let’s try to access Inbox:

inbox = outlook.GetDefaultFolder(number)

This function takes a number/integer as input which will tell the index of the inbox folder in our outlook app.

To check the index of all folders, just run this code snippet:

import win32com.client outlook=win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI") for i in range(50): try: box = outlook.GetDefaultFolder(i) name = box.Name print(i, name) except: pass


3 Deleted Items 4 Outbox 5 Sent Items 6 Inbox 9 Calendar

As you can see in the output Inbox index is 6. So we will use 6 in the function.

inbox = outlook.GetDefaultFolder(6)

If you want to print the subject of all the emails in the inbox, use this:

messages = inbox.Items # get the first email message = messages.GetFirst() # to loop through all the email in the inbox while True: try: print(message.subject) # get the subject of the email message = messages.GetNext() except: message = messages.GetNext()

There are other properties also like “message. subject”, “message. senton”, which can be used accordingly.

Downloading Attachment

If you want to print all the names of attachments in a mail:

for attachment in message.Attachments: print(attachment.FileName)

Let’s download an attachment (an excel file with extension .xlsx) from a specific sender.

import win32com.client import re import os outlook = win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI") inbox = outlook.GetDefaultFolder(6) messages = inbox.Items message = messages.GetFirst() while True: try: if'Data Report', str(message.Subject).lower()) != None and"ABC prasad", str(message.Sender).lower()) != None: attachments = message.Attachments for attachment in message.Attachments: if ".xlsx" in attachment.FileName or ".XLSX" in attachment.FileName: attachment_name = str(attachment.FileName).lower() attachment.SaveASFile(os.path.join(download_folder_path, attachment_name)) else: pass message = messages.GetNext() except: message = messages.GetNext() exit Explanation

This is the complete code to download an attachment from Outlook inbox. Inside try block, you can change conditions. For example, I am searching for those mails which have subjects such as Data Report and Sender name “ABC prasad”. So, it will iterate from the first mail in the inbox, and if the condition gets true, it will then look if that particular mail has an attachment with the extension .xlsx or .XLSX. So you can change all these things subject, sender, file type and download the file you want. Once it finds the file, it is saved to a path given as “download_folder_path”.

End Notes

We discussed three projects in a previous article and three in this article. I hope these python projects with codes helped you to polish your skill set. Just do some hands-on and try these; you will enjoy coding them. I hope you find this article helpful. Let’s connect on Linkedin.

Thanks for reading 🙂

Happy coding!

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