Certified Course on Master of Python for Future Professionals (A-Z)

Certified Course on Master of Python for Future Professionals (A-Z)

This course is designed for individuals aiming to build a solid foundation in Python and advance their careers in fields such as web development,data science,machine learning,automation,Deep Learning.

course at a glance

  • Date : 4 Aug - 31 Mar 2025
  • No. of Classes/ Sessions : 85
  • Total Hours : 255
  • Last Date of Registration : 30 Jul 2024
  • Class Schedule :
    • Sunday - 5:30 PM - 8:30 PM
    • Tuesday - 5:30 PM - 8:30 PM
    • Thursday - 5:30 PM - 8:30 PM
  • venue : Venue: BASIS Institute of Technology & Management Limited BDBL Bhaban (3rd Floor - East), 12 Kawran Bazar, Dhaka -1215. Contact Number: 01989408040

Price: TK. 85,000
(including VAT & TAX)
Discount Fee: 60,000 (One-time full payment: BDT 45,000)

Certified Course on Master of Python for Future Professionals (A-Z)

Course Duration: 8 Months
Total: 8 Modules

Admission Requirements:

  • Minimum H.S.C from any group or discipline
  • 1 Passport size photograph
  • Photocopy of Certificate & Mark sheet

Tuition fees Breakdown:

  • Admission Form: Tk. 500.00 (non refundable)
  • Tuition Fees: Tk. 60,000.00 for the program (Waiver applicable only on this amount)
    Total cost of the program: Tk. 60,500.00

Waiver on tuition fees:

  • 10% off for BITM graduates.
  • 10% off for BASIS member company employees.
  • 10% off for BASIS student forum members.
  • 10% off for a group of 3 or more employees from the same organization.
  • 25% off for paying full tuition fees at the time of admission. (Total Tk. 45,000)

Note: Students will be eligible for any ONE of the above waiver options.

Mode of Payment:

  • Installment 1: BDT 15,500 (At the time of admission)
  • Installment 2: BDT 15,000 
  • Installment 3: BDT 15,000 
  • Installment 4: BDT 15,000 

Course Description: Join our "Certified Course on Master of Python for Future Professionals (A-Z)" and embark on an in-depth journey to master Python, one of the most versatile and powerful programming languages in the tech industry today. This meticulously designed course caters to both beginners and experienced programmers, offering a thorough guide from the basics to advanced concepts. Students will gain extensive knowledge and practical skills in Python, preparing them for professional roles in various fields such as web development, software development, AI development, and data science. The curriculum covers key areas including NLP, machine learning, deep learning, automation, and artificial intelligence. The course is structured into several modules, each focusing on different aspects of Python programming, ensuring a well-rounded and comprehensive learning experience. 

Learning Outcomes: By the end of the "Certified Course on Master of Python for Future Professionals (A-Z)," participants will:

  1. Proficiency in Python Programming:
    • Understand and apply Python syntax and semantics.
    • Write efficient and effective Python code for various applications.
  1. Advanced Problem-Solving Skills:
    • Develop algorithms and data structures to solve complex problems.
    • Implement solutions using Python's powerful libraries and frameworks.
  1. Mastery of Object-Oriented Programming:
    • Design and implement Python classes and objects.
    • Utilize inheritance, polymorphism, encapsulation, and abstraction in Python projects.
  1. Expertise in Data Handling:
    • Read, write, and manipulate different file formats (CSV, JSON, etc.).
    • Handle exceptions and manage errors effectively in Python.
  1. Data Science and Analysis:
    • Use libraries like NumPy, pandas, and Matplotlib for data analysis.
    • Perform exploratory data analysis (EDA) and visualize data insights.
  1. Web Development Skills:
    • Develop web applications using Flask and Django frameworks.
    • Deploy web applications and understand web programming fundamentals.
  1. Automation and Scripting:
    • Automate repetitive tasks using Python scripts.
    • Work with APIs and perform web scraping to gather data.
  1. Machine Learning Competence:
    • Understand machine learning concepts and techniques.
    • Build, train, and evaluate machine learning models using scikit-learn and TensorFlow.
  1. Fundamentals of AI:
    • Understand core AI concepts and principles.
    • Familiarize with AI applications and use cases across various industries.
  1. AI Development:
    • Implement AI algorithms and techniques in Python.
    • Develop AI-driven applications using Python libraries and frameworks.
  1. NLP Basics:
    • Understand the fundamentals of natural language processing.
    • Work with text data, including tokenization, stemming, and lemmatization.
  1. NLP Libraries:
    • Use Python libraries such as NLTK, spaCy, and Hugging Face Transformers.
    • Perform tasks like sentiment analysis, named entity recognition, and text classification.
  1. NLP Models:
    • Build and train NLP models for various applications.
    • Implement advanced NLP techniques like word embeddings and sequence models.
  1. Image Processing Fundamentals:
    • Understand the basics of image processing and computer vision.
    • Work with image data using Python libraries.
  1. Image Processing Libraries:
    • Use libraries such as OpenCV and PIL (Pillow) for image manipulation.
    • Perform tasks like image filtering, edge detection, and feature extraction.
  1. Advanced Image Processing:
    • Implement image segmentation, object detection, and recognition techniques.
    • Develop and evaluate image processing pipelines for real-world applications.
  1. Deep Learning Basics:
    • Understand the principles of deep learning and neural networks.
    • Learn about different types of neural networks, including CNNs, RNNs, and GANs.
  1. Deep Learning Frameworks:
    • Use frameworks such as TensorFlow and Keras to build deep learning models.
    • Train and optimize deep learning models for various tasks.
  1. Advanced Deep Learning Techniques:
    • Implement transfer learning, fine-tuning, and model evaluation.
    • Develop deep learning models for complex applications such as image classification, object detection, and natural language processing.
  1. Introduction to Tkinter:
    • Understand the basics of Tkinter for GUI development.
    • Learn to create simple graphical user interfaces using Tkinter.
  1. Tkinter Widgets and Layouts:
    • Use Tkinter widgets like buttons, labels, text boxes, and more.
    • Implement layout management using frames, grids, and pack options.
  1. Advanced Tkinter Applications:
    • Develop advanced GUI applications with Tkinter.
    • Implement event handling, canvas drawing, and other interactive features.
  1. Hands-On Project Experience:
    • Apply learned skills in hands-on projects, gaining practical experience.
    • Complete a capstone project to demonstrate proficiency and knowledge.

Course Overview: This course covers everything you need to know about Python, ensuring a robust understanding of the language and its practical applications. With a focus on real-world projects and industry standards, you will gain the skills necessary to excel in various domains such as web development, data science, machine learning, automation, and more.


Module-1: Basic Python for All

Course Objectives: "Basic Python for All" involves outlining the key skills and knowledge that students should acquire by the end of the course. Here are some potential course objectives:

  • Explain the basic concepts of Python programming.
  • Identify and use Python's fundamental data types (integers, floats, strings, lists, tuples, dictionaries, sets).
  • Create and manipulate variables.
  • Implement control flow using conditional and loop statements
  • Define and call functions.
  • Understand and use function arguments and return values.
  • Import and use standard Python modules.
  • Understand file handling concepts including modes (read, write, append) and context managers.
  • Work with built-in data structures (lists, dictionaries, sets) and basic algorithms for manipulating these structures (sorting, searching).
  • Understand the principles of OOP.
  • Plan, develop, and test a small-scale project.
  • Apply the knowledge gained throughout the course to solve real-world problems.

Topics Covered:

    • Understanding Python syntax and semantics
    • Installing and setting up the Python environment
    • Writing and executing Python scripts
    • Variables, Input and Output
    • Operators
    • String manipulation
    • Control Statement (Conditions and Loops)
    • Lists, tuples, dictionaries, and sets
    • Functions
    • Basic algorithms and their implementation in Python
    • File Handling
    • Object-Oriented Programming
    • Python Modules and Packages

Module-2: Advanced Python

Course Objectives: Course objectives for an Advanced Python course involves focusing on deeper aspects of the language, advanced features, and practical applications. Here is a set of course objectives for such a course:

  • Comprehend Python's memory management, garbage collection, and object-oriented features.
  • Understand the intricacies of the Python interpreter and how it executes code.
  • Master advanced data structures such as heaps, balanced trees, and graphs.
  • Implement efficient algorithms for searching, sorting, and manipulation of complex data.
  • Apply functional programming concepts in Python using map, filter, reduce, and lambda functions.
  • Utilize higher-order functions and immutable data structures for clean and efficient code.
  • Understand the Global Interpreter Lock (GIL) and its implications.
  • Implement multi-threading and multi-processing to optimize performance.
  • Utilize asynchronous programming techniques with asyncio and other libraries.
  • Use decorators to modify or extend function behavior.
  • Understand metaclasses and their use in dynamic class creation.
  • Employ design patterns (e.g., Singleton, Factory, Observer) in Python.
  • Implement mixins, abstract base classes, and multiple inheritance effectively.

Topics Covered:

  • Python Package
  • Advanced Python OOP
  • Python Exception Handling
  • Advanced Conditional Statements
  • Regular Expressions,
  • Memory Management and Testing.
  • Network and Internet Programming
  • The request module
  • JSON
  • The SQLite Module

Module-3: Desktop Application Development with Python GUI Using TKinter

Course Objectives: Here are some possible course objectives for a course on Desktop Application Development with Python GUI using Tkinter:

  • Understand the basics of the Tkinter library and its role in Python GUI development.
  • Learn how to set up and configure a development environment for Tkinter applications.
  • Learn to create and use basic Tkinter widgets such as labels, buttons, entries, and text boxes.
  • Understand widget properties and methods to control their appearance and behavior.
  • Master the different layout management techniques (pack, grid, place) to organize widgets in a window.
  • Design and implement complex window layouts.
  • Use advanced widgets such as menus, toolbars, treeviews, and canvases.
  • Customize the appearance and behavior of widgets using options and styles.
  • Create forms and dialogs for data entry.
  • Implement data validation and error handling to ensure data integrity.
  • Use file dialogs to open and save files.
  • Implement file I/O operations to read from and write to files.
  • Load, display, and manipulate images within the application.
  • Set window icons and create image buttons.
  • Create and configure menus and toolbars for application navigation and functionality.
  • Implement context menus and keyboard shortcuts.
  • Learn to use the Canvas widget to draw shapes, lines, and text.
  • Implement interactive graphics and animations.
  • Create custom dialog boxes for user input and messages.
  • Use standard message boxes for alerts, confirmations, and errors.
  • Connect to databases and perform CRUD (Create, Read, Update, Delete) operations.
  • Use SQLite and other databases with Tkinter applications.
  • Design and implement a complete desktop application project.
  • Apply all learned concepts to build a functional and polished GUI application.

Topics Covered:

  • Introduction to Tkinter and Python GUI Development
  • Basic Tkinter Widgets
  • Layout Management
  • Event Handling
  • Advanced Tkinter Widgets
  • Styling and Theming
  • Data Handling and Storage
  • Enhancing User Experience
  • Advanced Topics
  • Deployment
  • Project Development

Module-4: Web Application Development Using Django Framework

Course Objectives:  Here are some possible course objectives for a course on Web Application Development Using the Django Framework:

  • Gain a comprehensive understanding of the Django framework and its components.
  • Learn to set up the development environment, including installing Django and required dependencies.
  • Understand the structure of a Django project and how to organize code within an application.
  • Learn to integrate databases with Django, including setting up models and performing migrations.
  • Understand how to use Django's ORM to interact with the database, including creating, reading, updating, and deleting records.
  • Gain knowledge of URL routing and how to create views to handle requests and responses.
  • Learn to create and manage HTML templates and serve static files like CSS, JavaScript, and images.
  • Understand how to create and handle forms in Django, including validation and processing user input.
  • Learn to implement user authentication and authorization, including user registration, login, and permissions.
  • Gain proficiency in using and customizing the Django admin interface for managing application data.
  • Develop skills to write and run tests for Django applications to ensure code quality and reliability.
  • Learn to deploy Django applications to production, including configuring web servers and setting up hosting environments.
  • Understand security best practices in Django to protect applications from common vulnerabilities.
  • Apply the knowledge gained by developing a fully functional web application as a capstone project.

Topics Covered:

  • Introduction to Django
  • Django Project Structure
  • Django Apps
  • URL Routing
  • Views
  • Templates
  • Models and Databases
  • Forms
  • Authentication and Authorization
  • Django Admin
  • Static and Media Files
  • Testing
  • Security
  • Deployment
  • Building RESTful APIs
  • Working with Third-Party Packages
  • Performance Optimization
  • Advanced Topics
  • Real-World Project Development

Module-5: AI Development Using Python (Image Processing)

Course Objectives: Here are some possible course objectives for a course on AI Development Using Python with a focus on Image Processing:

  • Gain a foundational understanding of artificial intelligence and its applications in image processing.
  • Develop proficiency in Python programming, focusing on libraries and tools commonly used in AI and image processing.
  • Learn the fundamental concepts of image processing, including image representation, color spaces, and basic transformations.
  • Gain hands-on experience with popular Python libraries such as OpenCV, PIL/Pillow, and scikit-image.
  • Understand techniques for image enhancement, including filtering, edge detection, and histogram equalization.
  • Learn methods for image segmentation and object detection, including thresholding, contour detection, and bounding boxes.
  • Explore techniques for extracting and describing features from images, such as SIFT, SURF, and ORB.
  • Learn techniques for preprocessing and augmenting image data to improve model performance.
  • Understand the concept of transfer learning and how to use pre-trained models for image processing tasks.
  • Explore advanced techniques such as image generation with GANs, image super-resolution, and image-to-image translation.

Topics Covered:

  • Introduction to AI and Image Processing
  • Image Processing Fundamentals
  • Working with Image Processing Libraries
  • Image Enhancement Techniques
  • Image Segmentation and Object Detection
  • Feature Extraction and Description
  • Introduction to Machine Learning for Image Processing
  • Building Image Classification Models
  • Preprocessing and Augmenting Image Data
  • Transfer Learning and Pre-trained Models
  • Advanced Image Processing Techniques
  • Model Evaluation and Optimization
  • Real-World Applications of Image Processing
  • Ethics and Responsible AI Development
  • Project Development

Module-6: AI Development Using Python (Natural language Processing (NLP))

Course Objectives: Here are some course objectives for a course on AI Development Using Python with a focus on Natural Language Processing (NLP):

  • Understand the basics of NLP and its applications.
  • Explore the challenges and opportunities in processing human language.
  • Review foundational Python programming skills relevant to NLP.
  • Introduce essential libraries for NLP tasks (NLTK, spaCy, TextBlob).
  • Learn techniques for cleaning and preparing text data.
  • Handle tokenization, stop word removal, stemming, and lemmatization.
  • Understand probabilistic models such as n-grams and language models.
  • Apply statistical techniques for text classification and sentiment analysis.
  • Introduction to machine learning algorithms for NLP tasks.
  • Implement supervised and unsupervised learning models (e.g., SVMs, Naive Bayes, clustering).
  • Introduction to neural networks for NLP (e.g., Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Transformer models).
  • Implementing deep learning models for tasks like sequence tagging, text generation, and machine translation.
  • Understand different text representations (Bag-of-Words, TF-IDF, word embeddings).
  • Learn techniques for feature engineering in NLP tasks.
  • Introduction to NER and entity linking tasks.
  • Implementing models for identifying and linking named entities in text.
  • Learn methods for sentiment analysis and opinion mining in text data.
  • Implement sentiment classification models using supervised and unsupervised approaches.
  • Understand topic modeling techniques (e.g., Latent Dirichlet Allocation (LDA)).
  • Implement topic modeling for discovering latent topics in text corpora.
  • Learn techniques for text summarization (extractive and abstractive).
  • Implement models for generating coherent text based on input data.
  • Introduction to machine translation and language understanding tasks.
  • Implement models for translation and comprehension using modern NLP techniques.
  • Apply the knowledge gained by developing practical NLP projects.
  • Work on real-world datasets and solve meaningful NLP problems.

Topics Covered:

  • Introduction to Natural Language Processing (NLP)
  • Python Basics for NLP
  • Text Preprocessing
  • Text Representation
  • Language Models
  • Named Entity Recognition (NER) and Entity Linking
  • Part-of-Speech Tagging (POS)
  • Syntax and Parsing
  • Sentiment Analysis
  • Text Classification
  • Information Extraction
  • Topic Modeling
  • Text Summarization
  • Dialogue Systems and Chatbots
  • Machine Translation
  • Question Answering Systems
  • Bangla NLP
  • Project Development

Module-7: AI Development Using Python (Machine Learning)

Course Objectives: Here are some course objectives for a course on AI Development Using Python with a focus on Machine Learning:

  • Understand the basic concepts and terminology of machine learning.
  • Differentiate between supervised, unsupervised, and reinforcement learning.
  • Review foundational Python programming skills relevant to machine learning.
  • Introduce essential libraries for machine learning (NumPy, Pandas, Matplotlib).
  • Learn techniques for cleaning, transforming, and exploring data.
  • Handle missing values, outliers, and feature scaling.
  • Implement and understand supervised learning algorithms:
    • Regression (Linear Regression, Polynomial Regression)
    • Classification (Logistic Regression, k-Nearest Neighbors (k-NN), Decision Trees, Random Forest, Support Vector Machines (SVM))
  • Understand metrics for evaluating machine learning models (e.g., accuracy, precision, recall, F1-score, ROC-AUC).
  • Implement cross-validation and hyperparameter tuning techniques.
  • Implement and understand unsupervised learning algorithms:
    • Clustering (K-means, Hierarchical Clustering)
    • Dimensionality Reduction (PCA, t-SNE)
  • Techniques for feature extraction, transformation, and selection.
  • Understand the impact of feature engineering on model performance.
  • Understand time series data and its characteristics.
  • Implement time series forecasting models (e.g., ARIMA, Prophet).
  • Apply machine learning techniques to real-world datasets and problems.
  • Develop end-to-end machine learning projects from data collection to deployment.

Topics Covered:

  • Introduction to Machine Learning
  • Python Basics for Machine Learning
  • Data Preprocessing
  • Supervised Learning Algorithms
  • Model Evaluation and Selection
  • Unsupervised Learning Algorithms
  • Feature Engineering and Selection
  • Introduction to Neural Networks
  • Project Development

Module-8: AI Development Using Python (Deep Learning)

Course Objectives: Here are some course objectives for a course on AI Development Using Python with a focus on Deep Learning:

  • Understand the fundamentals of deep learning and its applications.
  • Differentiate between traditional machine learning and deep learning approaches.
  • Review essential Python programming skills required for deep learning.
  • Introduce key libraries for deep learning (NumPy, Pandas, Matplotlib).
  • Learn the basic components of neural networks:
    • Neurons, layers, activation functions.
  • Implementing a simple feedforward neural network from scratch.
  • Overview of popular deep learning frameworks:
    • TensorFlow, Keras, PyTorch.
  • Setting up the development environment and using these frameworks.
  • Understand the architecture and principles behind CNNs.
  • Implementing CNNs for image classification tasks.
  • Learn the architecture and applications of RNNs.
  • Implementing RNNs for sequence prediction tasks (e.g., time series forecasting, text generation).
  • Understand the challenges of training RNNs and the role of LSTMs.
  • Implementing LSTMs for handling long-term dependencies in sequential data.
  • Introduction to autoencoders for unsupervised learning and dimensionality reduction.
  • Overview of GANs for generative modeling and image synthesis.
  • Understand transfer learning and its benefits in deep learning.
  • Fine-tuning pre-trained models (e.g., VGG, ResNet) for specific tasks.
  • Apply deep learning techniques to real-world datasets and problems.
  • Develop end-to-end deep learning projects from data preprocessing to model deployment.

Topics Covered:

  • Introduction to Deep Learning
  • Python Basics for Deep Learning
  • Neural Networks Basics
  • Deep Learning Frameworks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
  • Autoencoders and Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Transfer Learning and Pre-trained Models
  • Deep Reinforcement Learning
  • Project Development

Module-9: Data Science Using Python (Data Analytics and Data Visualization)

Course Objectives: Here are some course objectives for a course on Data Science Using Python with a focus on Data Analytics and Data Visualization:

  • Understand the role of data science in solving real-world problems.
  • Learn the basics of Python programming for data analysis.
  • Introduce essential libraries: NumPy, Pandas, Matplotlib, Seaborn.
  • Learn to manipulate and analyze data efficiently using these libraries.
  • Techniques for handling missing data, outliers, and duplicates.
  • Data transformation and normalization for analysis.
  • Learn methods to summarize, visualize, and interpret data.
  • Use statistical techniques to uncover patterns and relationships in data.
  • Principles of effective data visualization.
  • Create various types of plots and charts using Matplotlib and Seaborn.
  • Calculate summary statistics (mean, median, variance, etc.).
  • Understand probability distributions and hypothesis testing.
  • Understand the difference between classification and regression tasks.
  • Implement and evaluate regression models (linear, polynomial).
  • Develop and assess classification models (logistic regression, decision trees).
  • Clustering techniques (K-means, hierarchical clustering).
  • Dimensionality reduction methods (PCA, t-SNE).
  • Interactive and dynamic visualizations using Plotly and Bokeh.
  • Visualizing geospatial data and time series data.
  • Communicate insights and findings effectively through data visualization.
  • Presenting analytical results to stakeholders in a clear and compelling manner.
  • Introduction to handling large datasets with tools like Dask and Spark.
  • Implementing parallel computing for faster data processing.
  • Basics of SQL for querying relational databases.
  • Overview of NoSQL databases like MongoDB for handling unstructured data.
  • Collecting data from web sources using BeautifulSoup and Scrapy.
  • Accessing and integrating data from APIs (e.g., RESTful APIs).
  • Apply data science techniques to real-world datasets and problems.
  • Develop and present a comprehensive data analysis project.

Topics Covered:

  • Introduction to Data Science and Python
  • Python Libraries for Data Science
  • Data Cleaning and Preprocessing
  • Exploratory Data Analysis (EDA)
  • Statistical Analysis
  • Financial Data Analysis
  • Data Visualization Techniques for Statistical and Financial Data
  • Advanced Data Visualization for Statistical and Financial Data
  • Big Data Analytics with Python
  • Web Scraping and API Integration
  • Project Development

Why Choose This Course?

  • Comprehensive Curriculum: Covering everything from basic syntax to complex algorithms and data structures, this course ensures you have a solid foundation in Python programming.
  • Hands-On Projects: Apply what you've learned through real-world projects and practical exercises that build your confidence and problem-solving skills.
  • Expert Instructors: Learn from industry experts with years of experience in Python programming and software development.
  • Flexible Learning: Access the course materials anytime, anywhere, allowing you to learn at your own pace and convenience.
  • Certification: Earn a recognized certification upon completion, showcasing your Python expertise to potential employers.

Enroll Now and Start Your Journey!

Don't miss out on the opportunity to master Python and pave the way for a successful future in technology. Enroll in our "Certified Course on Master of Python for Future Professionals (A-Z)" today and take the first step towards becoming a Python expert!

Who Should Enroll?

  • Aspiring Programmers: Individuals with no prior programming experience who want to start their journey with Python.
  • Career Changers: Professionals looking to transition into a tech-focused career and gain valuable programming skills.
  • Students and Graduates: Students pursuing degrees in computer science, engineering, or related fields who want to strengthen their Python skills.
  • Tech Enthusiasts: Anyone passionate about technology and looking to stay ahead in the rapidly evolving tech landscape.

Join a community of learners and professionals who are transforming their careers with Python. Enroll now and start your journey to Python mastery!

For more information to register or visit our office.

Invest in Your Future. Master Python Today!

Curriculum

Certified Course on Master of Python for Future Professionals (A-Z) Certified Course on Master of Python for Future Professionals (A-Z) 255 Hrs

Tentative Class Start

4th August, 2024

Available Seat

10 / 25

who can join

Who has completed at least H.S.C from any discipline and have basic computer knowledge.

Meet the Instructor