23 courses for Python

Contain 25 videos having brief knowledge about python programming and live projects

Unlock the power of Python programming with a structured, hands-on approach. This course will guide you through: The fundamentals of Python syntax and concepts Writing efficient Python code for real-world applications Working with data, loops, functions, and libraries Building projects that reinforce your learning Tips and best practices from industry experts to write clean, maintainable Python code Whether you’re starting from scratch or looking to sharpen your skills, this course provides a clear path to becoming a confident Python programmer.

Complete Python Programming Course – Recorded Classes 📅 50+ Sessions | Beginner to Advanced | Projects Included This course is designed to take you from zero to production-ready Python developer. All sessions are hands-on, code-driven, and project-based, with recordings, notes, and assignments included. Core Python (Beginner → Intermediate) First Python Code, Virtual Environment Conditional Statements, Loops (for, while, break, continue) Lists, Tuples, Dictionaries, Sets List Comprehensions, Pattern Problems File Processing: Text, CSV, JSON Object-Oriented Programming (OOP) Classes, Objects, Attributes, Methods Self & __init__ deep dive Abstraction, Encapsulation, Inheritance Polymorphism & Advanced OOP Advanced Python Exception Handling (basic → custom exceptions) Functions: Basics, Advanced, Lambdas, Closures Regex (basic → advanced) Logging (basic, custom handlers) Dunder/Magic Methods Data & EDA Pandas, NumPy, Matplotlib, Seaborn Data Collection & Inspection Handling Missing Values (imputation, filling) JSON Data Parsing Projects Extras & Real-World Skills Async & Multithreading basics PostgreSQL Connection Full-stack Python Projects with Deployment Docker, GitHub Actions, Unit Testing/Pytest Good Coding Practices, Linting, Type Hinting Lifetime access to recordings Code, assignments & projects included Updated continuously with new sessions

No prior coding experience? No problem! This beginner-friendly Python for Data Science course equips you with the fundamental programming skills to launch into data science. Learn core Python concepts, explore essential libraries like NumPy and Pandas, and master data manipulation and visualization using Jupyter Notebooks. By the end, you'll be writing Python scripts and transforming data into insights.

Eligible : Open To all Freshers & College Students (UG,PG) What We Offer ? (Mode : Online Remote ) ✅ Internship + Training with Offer Letter & Course Completion Certificate ✅ ₹25,000 Per Month Internship For 6 Months After Course Completion . ✅PPO up to 4.5–8 LPA( Based on internship performance ) ✅ Duration: 1 to 6 Months. ✅ Weekend Live Session: Every Sunday at 6 PM ✅ Letter of Recommendation Included ✅ 100% Placement Support After Course + Projects ✅ 4 Minor & 2 Major Real-Time Projects ✅ Lab Sessions for Every Chapter ✅ Python Programming: Scratch to Advanced Level ✅ Recorded Theory & Lab Sessions + Weekly Assignments 📘 Python Course Index – Scratch to Advance Introduction to Python Python Installation & Setup Basic Syntax and Structure Variables and Data Types Operators in Python Input, Output & Type Casting Control Statements (if, elif, else) Loops (for, while) Functions & Lambda Expressions Lists, Tuples, Sets & Dictionaries Strings & String Methods Modules & Packages File Handling Exception Handling Object-Oriented Programming (OOP) Python Libraries (OS, Math, Random, etc.) Virtual Environments & PIP Python Data Structures (Advanced) Regular Expressions (Regex) Python Decorators & Generators Working With JSON & APIs Python Database Connectivity (MySQL / SQLite) Python for Data Analysis (Pandas, NumPy) Python for Visualization (Matplotlib) Python for Automation (Scripting) Testing & Debugging in Python Mini Projects Major Projects (Advanced)

Want to learn Python fast and effectively? This 1.5-hour crash course is designed to teach you the core Python concepts visually using animations, RAM execution insights, and live code tracing. ✅ What You’ll Learn: Python variables & data types Lists, tuples, sets, and dictionaries Control flow (if-else, loops) Functions & lambda functions String manipulation This course is perfect for beginners and non-IT learners looking for an easy, engaging, and visual way to understand Python. Start coding with confidence today!

Eligible : Open To all Freshers & College Students (UG,PG) What We Offer ? (Mode : Online Remote ) ✅ Internship + Training with Offer Letter & Course Completion Certificate ✅ ₹25,000 Per Month Internship For 6 Months After Course Completion. ✅PPO up to 4.5–8 LPA( Based on internship performance ) ✅ Duration: 1 to 6 Months . ✅ Weekend Live Session: Every Sunday at 6 PM ✅ Letter of Recommendation Included ✅ 100% Placement Support After Course + Projects ✅ 4 Minor & 2 Major Real-Time Projects ✅ Lab Sessions for Every Chapter ✅ Python Full Stack Web Development : Scratch to Advanced Level ✅ Recorded Theory & Lab Sessions + Weekly Assignments 📘 Python Full Stack Web Development Course Index – Scratch to Advance Introduction to Full Stack Development Python Programming Basics Advanced Python Concepts Git & GitHub Frontend Development (HTML, CSS, JavaScript) Advanced JavaScript Backend Development with Python Frameworks (Flask/Django) REST API Development Database Management (SQL & ORM) Frontend–Backend Integration Full Stack Project Development Deployment & Hosting Testing & Debugging Advanced Topics in Full Stack Minor & Major Projects Career Preparation & Interview Training

4+ hours of lectures to help you learn the basics. You don't know "P" of python? You don't know "T" of Technology? No problem. This is made for people who want to learn. It doesn't matter if you are in school/Non-IT professional, starting your journey etc. Topics covered: What is python? Why to use? Variables Data types: Int, Float, Strings String Methods Type Conversion Lists Lists Methods Tuples Tuples Methods For loops Functions Numpy - Basics + Additional Resources! You can work as a Data Analyst, Data Scientist, Data Engineer, SDE etc by using Python!

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💡 Looking to level up your career in data analytics, development, or automation? Join our Python and SQL Online Training Course and learn directly from expert instructors in real-time! 📚 What You’ll Learn: ✅ Python Programming: From basics to advanced concepts. ✅ SQL Mastery: Queries, joins, functions, and database management. ✅ Live coding sessions and hands-on projects. ✅ Practical tips for optimizing code and queries. 🌐 Language: Available in both English & Tamil. 🖥️ Mode: Live, interactive online classes. ⏰ Flexible Schedule: Classes available on both weekdays and weekends to suit your convenience! 📈 Who Should Join? Aspiring Data Analysts and Developers. Beginners looking to kickstart their coding journey. Professionals wanting to enhance their skills. 🔗 Why choose online instructor-led training? Real-time interaction with the trainer. Flexible scheduling. Lifetime access to course materials. 👉 Enroll today to take advantage of this special offer and unlock your potential with Python & SQL! 📞 Contact Us for More Details and Enrollment: 📧 Email: 📱 Phone/WhatsApp: 8056296965 Feel free to share these details so learners can easily reach out to you! Let me know if you'd like help formatting them or making them more appealing. #PythonTraining #SQLTraining #OnlineCourses #InstructorLedTraining #DataAnalytics #CodingSkills #CareerGrowth #SpecialOffer

Welcome to Learning Python with Codey! A fun storybook that teaches Core Python basics to kids. Meet Codey, a friendly little python snake, and his magical laptop friend Luma. Together, they go on an adventure through the forest to learn coding step by step. Each chapter includes a tiny lesson, a simple Python spell, and a colorful picture.

This study pack provides a complete, practical introduction to Credit Risk Modeling using Generative AI, with a strong focus on Python-based prompt automation. Designed for analysts, students, and professionals, it shows how to use advanced AI models and engineered prompts to automate the entire credit risk workflow from data preparation to final model documentation. Using a combination of Python, machine learning techniques, and powerful GenAI prompts, you will learn how to instantly generate code for data cleaning, feature engineering, PD/LGD/EAD model development, validation, and reporting. The study pack also demonstrates how to automate scorecard creation, regulatory documentation, model interpretation, and audit-ready outputs with minimal manual effort. Whether you are building Probability of Default (PD) models, Loss Given Default (LGD), or Exposure at Default (EAD) models, Generative AI helps you work faster, smarter, and more accurately. With ready-made prompts, Python scripts, templates, and automated workflows, this study pack empowers you to reproduce end-to-end credit risk models in minutes without compromising on quality or regulatory expectations. Ideal for beginners and experienced modelers alike, it combines real-world credit risk concepts, AI-driven automation, and hands-on Python implementation, enabling you to upgrade your technical skills and significantly boost productivity in credit risk modeling.

Your personal guide to coding success. With one-on-one mentorship and project-based learning, CodeMentor helps you master Python, Java, C, HTML, CSS, and more. Whether you’re starting fresh or aiming to upskill, we make coding simple, practical, and career-focused.

Topics covered - Day 1: Traditional NLP and Language Models Day 2: Generative AI and Prompt Engineering Day 3: Fine-Tuning and Optimization Day 4: Retrieval-Augmented Generation (RAG) Day 5: Evaluation and Emerging Trends This will include PPTs and python notebooks for hands-on exercise.

This course is beneficial and will fill the barriers between the company and the skilled labourers using gen AI for work and to secure their future ... as an AI engineer... In this course, I will be teaching you all Numpy, Jupyter, Pandas, Python Programming, Data Analysis, OOPs, Data Structure Algorithms, Application Programming Interface(API), Data Manipulation, Data Processing

This PDF is a beginner-friendly guide to mastering Pandas for data analysis. You will learn: • DataFrames & Series basics • Reading & writing CSV / Excel files • Data filtering & cleaning • Sorting & grouping data • Basic data visualization • Real-world practice examples Perfect for: • Students • Beginners in Data Science • Python learners • Interview preparation Instant download after purchase.

This course is designed to provide a comprehensive and hands-on introduction to machine learning. Students will learn the fundamental principles and techniques of machine learning, focusing on practical applications and real-world problem solving. By the end of the course, participants will be equipped with the skills necessary to build, evaluate, and deploy machine learning models. ### Course Objectives - Understand the core concepts of machine learning. - Gain practical experience with popular machine learning algorithms. - Learn to preprocess and visualize data. - Develop skills to evaluate and fine-tune models. - Understand the deployment of machine learning models in production environments. ### Course Outline #### Module 1: Introduction to Machine Learning - What is Machine Learning? - Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning - Applications of Machine Learning in Various Industries #### Module 2: Data Preprocessing and Exploration - Data Cleaning and Preparation - Handling Missing Values - Data Visualization Techniques - Feature Engineering and Selection #### Module 3: Supervised Learning Algorithms - Linear Regression - Logistic Regression - Decision Trees and Random Forests - Support Vector Machines (SVM) - Evaluation Metrics: Accuracy, Precision, Recall, F1 Score #### Module 4: Unsupervised Learning Algorithms - Clustering: K-means, Hierarchical Clustering - Dimensionality Reduction: PCA, t-SNE - Association Rule Learning: Apriori, Eclat #### Module 5: Advanced Topics in Machine Learning - Ensemble Methods: Bagging, Boosting, and Stacking - Neural Networks and Deep Learning - Natural Language Processing (NLP) - Time Series Analysis #### Module 6: Model Evaluation and Optimization - Cross-Validation Techniques - Hyperparameter Tuning: Grid Search, Random Search - Model Evaluation and Validation #### Module 7: Machine Learning with Python - Introduction to Python for Machine Learning - Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Keras - Building and Training Models in Python #### Module 8: Deploying Machine Learning Models - Introduction to Model Deployment - Deploying Models with Flask and Docker - Monitoring and Maintaining Models in Production ### Hands-on Projects - Predictive Modeling: House Price Prediction - Image Classification: Recognizing Handwritten Digits - Sentiment Analysis: Analyzing Movie Reviews - Customer Segmentation: Clustering E-commerce Data ### Prerequisites - Basic knowledge of Python programming. - Familiarity with statistics and probability. - Understanding of linear algebra and calculus is beneficial but not mandatory. ### Course Duration - 12 weeks, with 2 hours of instruction per week and 4 hours of practical work. ### Assessment - Weekly quizzes and assignments. - Mid-term project. - Final capstone project. ### Certification Upon successful completion of the course, participants will receive a certificate of completion, which can be shared on professional networks such as LinkedIn.

In this coսrse, we demystify AI concepts, algorithms, and tools. Yoս’ll grasр the fսndamental рrinciрles behind machine learning, natսral langսage рrocessing, comрսter vision, and more. Oսr exрert instrսctors gսide yoս throսgh рractical examрles, ensսring yoս սnderstand the “why” and “how” of AI. Who Is It For? This coսrse is рerfect for: Cross-fսnctional manager or business leader exрloring AI’s potential Technical or non-technical рrofessional Corрorate Execսtive or IT manager interested in sսccessfսl AI imрlementation in their рroject Prereqսisites While no рrior AI experience is necessary, a basic understanding of рrogramming (Python, рerhaрs?) will be advantageoսs. Bring your cսriosity, and we’ll handle the rest!

This is the exact AI roadmap I personally followed to crack 13 internships in AI at top companies like Amazon, IBM, EY, Infosys,etc — and land 6 job offers in the AI domain in under 60 days. From Python basics to deploying your own autonomous AI agents on the cloud, this step-by-step guide is designed to fast-track your AI career. Whether you’re starting your AI journey or aiming to level up your skills, this roadmap is designed to fast-track your success and make you job-ready in record time.

SAMPLE CERTIFICATE At SynergyWorks, we recognize the significance of practical knowledge and hands-on experience in the tech industry. That's why we're excited to present our Training-Based Data Analyst Internship Certification Program. This innovative opportunity enables individuals to enhance their skills and abilities through a comprehensive training process, leading to the acquisition of a valuable certification in just 30 days. Benefits of the Training-Based Data Analyst Internship Certification Program: Official Recognition: Earn a formal certification, validating your skills and expertise in data analysis. Professional Development: Enhance your resume and professional profile with tangible evidence of your capabilities. Cost-Effective: Accessible to all with minimal documentation charges, ensuring affordability. Flexible Schedule: Complete the training within 30 days, accommodating your busy schedule. Program Details: Duration: 30 Days Cost: Training Material Charges: Rs. 199 Certification: Upon completion of the training, participants will be awarded a digital Internship Certificate. How to Apply: To enrol in the Training-Based Data Analyst Internship Certification Program, fill out the provided form accurately. Upon receiving the payment, we will add you to google classroom within 24 hours. Important Note: No Refund Policy: Please note that the training material charges are non-refundable as they cover administrative costs and training materials. Communication via Email: Participants will receive all program-related information, updates, and notifications via email. Digital Internship Certificate: Upon completion of the training, participants will receive a digital Internship Certificate highlighting their achievements. Training Process: Enrolment: Enrol in the program by paying the fees. Study Material: Participants will receive daily PDF study materials via Google Classroom. This structured process ensures a seamless and efficient experience for all participants. Don't miss out on this opportunity to enhance your skills and advance your career with SynergyWorks' Training-Based Data Analyst Internship Certification Program! Apply now and kickstart your journey towards professional success. Day 1-5: Introduction to Data Analysis Day 1: Introduction to Data Analysis, Importance, and Applications Day 2: Basic Statistics - Mean, Median, Mode, Standard Deviation Day 3: Types of Data - Qualitative vs. Quantitative Day 4: Data Collection Methods and Sources Day 5: Introduction to Data Cleaning and Preprocessing Day 6-10: Excel for Data Analysis Day 6: Basics of Excel - Interface, Formulas, and Functions Day 7: Data Sorting, Filtering, and Validation Day 8: Pivot Tables and Pivot Charts Day 9: Data Visualization in Excel Day 10: Advanced Excel Functions and Macros Day 11-15: Introduction to SQL Day 11: Introduction to Databases and SQL Day 12: Basic SQL Queries - SELECT, FROM, WHERE Day 13: SQL Functions - COUNT, SUM, AVG, MIN, MAX Day 14: Data Manipulation with SQL - INSERT, UPDATE, DELETE Day 15: Joining Tables in SQL Day 16-20: Data Visualization Day 16: Introduction to Data Visualization - Importance and Tools Day 17: Basics of Matplotlib (Python) Day 18: Creating Visualizations with Seaborn (Python) Day 19: Introduction to Tableau - Interface and Basic Charts Day 20: Creating Dashboards in Tableau Day 21-25: Python for Data Analysis Day 21: Introduction to Python - Basics and Setup Day 22: Data Structures in Python - Lists, Tuples, Dictionaries Day 23: Pandas for Data Analysis - Series and DataFrames Day 24: Data Cleaning with Pandas Day 25: Data Aggregation and Group Operations with Pandas Day 26-30: Intermediate Data Analysis Techniques Day 26: Introduction to Machine Learning - Basic Concepts Day 27: Exploratory Data Analysis (EDA) - Techniques and Tools Day 28: Correlation and Regression Analysis Day 29: Time Series Analysis - Basics and Applications Day 30: Case Study - End-to-End Data Analysis Project