5 Days 5 Machine Learning Projects From Basic To Pro

Name: 5 Days 5 Machine Learning Projects From Basic To Pro
Rating: 3.9 (21 reviews)

Complete 5 Machine Learning Projects | Hands-On Machine Learning Project Course | Machine Learning Project From Scratch

Created byARUNNACHALAM SHANMUGARAAJAN

Last updated 5/2025

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What you'll learn

Understand the foundations of machine learning through hands-on projects.
Learn the end-to-end workflow of a machine learning project, from data preparation to model deployment.
Gain experience with popular machine learning libraries like scikit-learn, TensorFlow, and Pandas.
Explore diverse domains such as regression, classification, NLP, computer vision, and recommendation systems.

Course content

5 sections • 31 lectures • 1h 19m total length

Introduction To Project0:41
Develop an end-to-end resume analyzer project for machine learning jobs using natural language processing. Follow module-based guidance on importing packages, preparing the dataset, training the dataset, and predicting resume scores.
Resume Class 1 : Import Packages3:29
Explore building a resume analyzer for keywords and skills using NLP in Google Colab, including importing nltk, stopwords, tokenization, regex, and pdf tools for processing resumes.
Resume Class 2 : Import Dataset1:22
Create a sample resume analyzer for machine learning jobs by listing keywords such as Python, data analysis, machine learning, artificial intelligence, NLP, TensorFlow, and Keras, then remove English stopwords.
Resume Class 3 : Extract Text From Resume3:32
Upload your resume pdf to Google Colab, open it with a pdf reader, and extract text from all pages using a for loop.
Resume Class 4 : Tokenization3:15
Learn tokenization for a resume analyzer by building a function that lowers text, removes punctuation, tokenizes words, and filters out stop words to extract essential keywords and skills.
Resume Class 5 : Match Keywords & Skills1:30
Match resume keywords to predefined machine learning job keywords using token comparison and a for loop to identify matches and predict the resume score.
Resume Class 6 : Analyze Resume Score & Conclusion7:12
learn to build a resume analyzer that calculates a resume score by extracting text from pdfs, matching keywords, and revealing skills for machine learning job applications.

Introduction To Project0:42
Explore an e-commerce product category classification project with logistic regression, covering how to import the data set, train it, save the model, and predict outputs.
Ecommerce Class 1 : Import Packages3:26
A hands-on session to build an ecommerce product category classification using logistic regression in Google Colab, importing pandas, nltk stopwords, tf-idf vectorization, and training with train-test split to evaluate accuracy.
Ecommerce Class 2 : Import Dataset1:45
Import the e-commerce dataset for product title and category classification using logistic regression by loading a csv with pandas read_csv, then view head and tail to inspect the 23,000 values.
Ecommerce Class 3 : Data Preprocessing4:32
perform data pre-processing by removing null values, punctuation, and numbers, and convert text to lowercase. Apply English stopwords with nltk and create a clean_text function to create cleaned title column.
Ecommerce Class 4 : Train Dataset Using Logistic Regression2:37
Train a dataset with logistic regression by converting the cleaned title into numerical features using a tf-idf vectorizer, then split data into train and test sets and fit the model.
Ecommerce Class 5 : Output & Conclusion2:24
Complete an ecommerce product category classification with logistic regression, saving the trained model to product.pickle, then load with OpenCV to predict on x_test in real time and display accuracy.

Introduction To Project0:41
Emotion Class 1 : Import Packages4:12
Learn to set up emotion detection from text using NLP in Google Colab by importing nltk, stopwords, multinomial Naive Bayes, tfidf vectorizer, transformers, pandas, and train-test split, then assess accuracy.
Emotion Class 2 : Import Dataset1:01
Build a dictionary mapping text to emotion for an NLP based emotion detection project, then convert the data into a pandas data frame.
Emotion Class 3 : Dataset Preprocessing1:37
Define a function with def to remove unnecessary words, convert text to lowercase, create a new column with the preprocessing, and prepare the dataset for Naive Bayes training.
Emotion Class 4 : Train Dataset Using Naive Bayes3:19
Train the emotion dataset by separating features x and target y, using a vectorizer, splitting data with 0.2 test size and 42 random state, then train a multinomial nb classifier.
Emotion Class 5 : Output & Conclusion3:26
Predict emotion from text using a classifier and a hugging face pre-trained text classification model, then output scores for emotions like joy, anger, and sadness.

Introduction To Project0:51
Build an ad click prediction project with logistic regression across five modules: import packages and data, clean and preprocess, split features and target, train, and predict clicks.
AD Class 1 : Import Packages3:18
Set up a Google Colab notebook for an ad click prediction project using logistic regression, and import pandas, the logistic regression model, train_test_split, accuracy_score, and standard scaler.
AD Class 2 : Import Dataset2:01
Import a CSV dataset in Google Colab with pandas read_csv, preview rows with head and tail, and apply logistic regression to predict ad clicks from time spent and internet usage.
AD Class 3 : Dataset Preprocessing3:45
Remove the unnecessary columns from the csv, keep only essential features for ad click prediction, and prepare the features and target (x and y) for logistic regression.
AD Class 4 : Train Dataset Using Regression2:43
Learn to train a dataset with logistic regression by splitting into train/test (80/20, random state 42), scale features with StandardScaler, and fit the model on x_train and y_train.
AD Class 5 : Output & Conclusion2:05
Predict ad clicks with logistic regression, printing y predictions for x test and evaluating accuracy with accuracy_score on y_test vs y_prediction, achieving about 96 percent.

Introduction To Project0:35
Explore how to build a diabetes prediction project with logistic regression by importing data, training the model, and interpreting its outputs.
Diabetes Class 1 : Import Packages3:07
Set up a diabetes prediction project with logistic regression in Google Colab by creating a notebook, choosing runtime, and importing pandas, numpy, train_test_split, accuracy, standard scaler, and simple imputer.
Diabetes Class 2 : Import Dataset2:00
Import a diabetes dataset from csv into google colab with pandas; view top and bottom rows with head and tail to confirm 768 records and the outcome column (for classification).
Diabetes Class 3 : Data Preprocessing3:19
learn to preprocess diabetes data by replacing zeros with NaN and imputing missing values with the mean for glucose, blood pressure, skin thickness, insulin, and BMI.
Diabetes Class 4 : Train Dataset3:36
Train a diabetes dataset with logistic regression by separating features and outcome, performing a train-test split with 20% test and 80% training, applying scaling, and training the model.
Diabetes Class 5 : Output & Conclusion1:52
Complete the diabetes prediction project with logistic regression by generating the prediction from x_test and printing the output 00100, and report the 75 percent accuracy for the classifier.
Product Review Classification Project

Requirements

Basic Python & Machine Learning Required

Description

Welcome to the "5 Days 5 Machine Learning Projects From Scratch" course, a fast-paced, hands-on learning experience designed to take you from theory to practice in just five days! This course is perfect for aspiring data scientists, machine learning enthusiasts, and professionals who want to enhance their portfolio with real-world projects.

Each day focuses on building a fully functional machine learning project from scratch, covering various domains and techniques to give you a well-rounded experience. By the end of this course, you’ll have completed five diverse projects, gaining invaluable skills in data preprocessing, model building, evaluation, and deployment. This course emphasizes practical skills, critical thinking, and hands-on implementation to ensure you’re job-ready.

What You’ll Build:

Day 1: Create Project Using Natural Language Processing.
Day 2: Create Project Using logistic regression and neural networks.
Day 3: Create Project Using Naive Bayes classification.
Day 4: Create Project Using Medical Image Prediction.
Day 5: Create Project For Ad Click Through Prediction.

Whether you’re a student, professional, or self-taught learner, this course equips you with the knowledge and confidence to tackle real-world machine learning problems. Acquire skills to confidently implement machine learning solutions in real-world scenarios. Start your journey today and transform your passion for machine learning into tangible expertise!

By the end of this course, you will have completed 5 fully functional Machine Learning Projects and gained confidence to develop your own machine learning solutions.

Who this course is for:

Students
Python & Machine Learning Coders

5 Days 5 Machine Learning Projects From Basic To Pro

What you'll learn

Explore related topics

Course content

Introduction To Resume Analyzer Project For Machine Learning Job7 lectures • 21min

Ecommerce Product Category Classification Using Machine Learning6 lectures • 15min

Emotion Detection From Text Using Natural Language Processing6 lectures • 14min

AD Click Prediction Project Using Machine Learning6 lectures • 15min

Diabetes Prediction Project Using Machine Learning6 lectures • 14min

Requirements

Description

Who this course is for: