Data Science and Machine Learning using Python

Master Data Analytics, Visualization & Machine Learning with Hands-On Projects

About the Course:

The Data Science & Machine Learning Using Python course is a comprehensive, hands-on program designed to equip learners with the essential skills required to work with real-world data and build intelligent machine learning models.
Starting from the basics of Python programming, the course gradually advances into statistics, data processing, data visualization, and supervised machine learning algorithms.

Learners get industry-level exposure through practical labs, case studies, and a capstone project that simulates real problem-solving scenarios in data-driven industries.

Key Features:

• Fully Hands-On Training (50 Hours) with real datasets
• Covers End-to-End Data Science Workflow – Python → Statistics → Data Cleaning → ML
• Project-Oriented Learning with a full ML pipeline implementation
• Case Studies for Each ML Algorithm
• Industry-Relevant Curriculum aligned with modern DS & ML hiring needs
• Beginner-Friendly – Starts from Python basics
• Practical Labs for Every Module
• Experienced Industry Instructors

Skills Covered in This Course:

By the end of this course, learners will gain strong proficiency in:

Programming & Analytics

• Python programming
• File handling, functions, OOPs
• Numpy, Pandas, Seaborn, Matplotlib
• Data cleaning, data processing, feature engineering
• Descriptive & inferential statistics

Machine Learning

• Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Support Vector Machines (SVM)
• Model evaluation & metrics

Business & Analytical Skills

• Exploratory Data Analysis (EDA)
• Understanding trends, patterns, and anomalies
• Business problem framing
• Data-driven insights and decision-making

Who Can Join This Course?

This course is ideal for:

• Engineering & Science Students
• Early-career Professionals
• Working professionals shifting to Data Science
• Anyone interested in AI, Data Analytics, or ML
• Beginners who want to start a career in Python-based data analysis
• Career Opportunities After This Course

Learners can pursue roles such as:

• Data Analyst
• Machine Learning Engineer
• Python Developer (Data)
• Business Analyst
• Data Science Intern / Trainee
• Research Analyst
• AI/ML Project Assistant

With advanced learning, this path leads to high-demand roles like:

• Data Scientist
• ML Engineer
• AI Engineer
• Data Engineer

Course Curriculum with Hands-on Labs

Below is the structured curriculum with practical labs mapped to each module.

Module 1: Introduction to Data Science & AI

• AI, ML, DL & Data Science overview
• Who uses AI?
• Applications of ML in IT, banking, healthcare
• What makes a Machine Learning expert?
• Roadmap to becoming an ML developer

Hands-on Labs

• Identify ML applications in real life
• Mini research: Case study analysis of ML in a chosen industry

Module 2: Python Programming

• Python basics
• Variables, loops, functions
• Data structures: list, tuple, dictionary
• OOP concepts
• File handling
• Modules & packages
• Working with OS, time, datetime

Hands-on Labs

• Write Python programs to automate small tasks
• Build a calculator using functions
• File read/write operations
• OOP-based student management program

Module 3: Statistics for Machine Learning

• Population vs sample
• Descriptive & inferential statistics
• Mean, median, mode
• Variance, standard deviation, IQR
• Skewness & kurtosis
• Correlation analysis

Hands-on Labs

• Compute summary statistics using Python
• Visualize correlation between features
• Interpret shape, spread, and distribution

Module 4: Data Science with NumPy & Pandas

• Numpy arrays, indexing, slicing
• Linear algebra operations
• Pandas series, dataframe
• Importing CSV, Excel, JSON
• Data filtering & selection
• Boolean indexing

Hands-on Labs

• Perform array manipulation using NumPy
• Load datasets & perform data analysis
• Filter rows/columns using conditions

Module 5: Data Cleaning & Feature Engineering

• Handling duplicates & missing values
• Text data processing
• Creating new features
• Categorical feature creation
• Combining multiple datasets
• Groupby operations
• Apply functions

Hands-on Labs

• Clean a raw dataset (real-world CSV)
• Handle missing values using strategies
• Create new meaningful features

Module 6: Data Visualization (Matplotlib & Seaborn)

• Line plots, bar plots, scatter plots
• Boxplot, violin plot, heatmap
• Subplots
• Color, patterns, annotation

Hands-on Labs

• Visualize distribution of numeric features
• Build heatmap for correlation matrix
• Plot multi-category data using Seaborn

Module 7: Machine Learning – Linear Regression

• Predictive modeling
• Cost function
• Gradient Descent
• Multivariate regression
• Overfitting & regularization
• R², MAPE, RMSE

Hands-on Labs

• Build a Linear Regression model in Python
• Evaluate performance using metrics
• Apply L1 & L2 regularization

Module 8: Machine Learning – Logistic Regression

• Sigmoid function
• Confusion matrix
• Accuracy, recall, precision, F1 score
• ROC Curve, AUC
• Model significance

Hands-on Labs

• Train a logistic regression model
• Plot ROC curve
• Interpret classification report

Module 9: Machine Learning – KNN

• KNN classifier & regressor
• Distance metrics
• Overfitting & underfitting

Hands-on Labs

• Implement KNN using Scikit-learn
• Compare model accuracy with different K values

Module 10: Machine Learning – Decision Trees

• Tree building
• Entropy & Gini
• Regression trees
• Hyperparameter tuning
• GridSearch
• Tree visualization

Hands-on Labs

• Build & visualize a decision tree
• Tune parameters to avoid overfitting

Module 11: Machine Learning – SVM

• Working of SVM
• Kernel functions
• Optimization
• Slack variable
• Applications

Hands-on Labs

• Train a classifier using SVM
• Experiment with different kernels

Project Work

Learners will complete a full end-to-end ML project such as:

• Predicting house prices
• Credit card fraud detection
• Customer churn analysis
• Diabetes prediction

Project Steps

• Data cleaning
• EDA
• Model building
• Hyperparameter tuning
• Final report & presentation

What You Get

• 50 Hours of Hands-On Training
• Course Material (Notes + Codes + Dataset)
• Certification
• Weekly Assignments
• Real-World ML Project
• Lifetime Access to Learning Material

Contact us for more details:

Phone: +91-8341957746 / +91-7842670309

Email: electrocloudlabs@gmail.com

Website: www.electrocloudlabs.com