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 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 

Objective:

• Equip students with fundamental programming skills using Python.
• Understand basic syntax, data types, and structures in Python.
• Develop problem-solving skills through hands-on coding exercises.
• Create simple programs to solidify understanding of basic concepts.

Topics Covered:

• Python Installation and Environment Setup
• Basic about Python
• Python Basic and Problem Solving
• Problem Solving and Project Development
• Common Python Modules
• Python Packages and Modules
• Common Python Packages
• Python MySQL
• Problem Solving and Project Work

Module-2: Advanced Python

Objective:

• Develop proficiency in advanced Python programming.
• Explore complex data structures and algorithms.
• Utilize Python for more sophisticated and efficient programming tasks.
• Apply Python in various real-world scenarios and projects.

Topics Covered:

• Advanced Python OOP
• Python Exception Handling
• Advanced Python
• Network and Internet Programming
• The request module
• JSON
• The SQLite Module
• Review All

Module-3: Python GUI Using TKinter

Objective:

• Create graphical user interfaces (GUIs) using Tkinter.
• Understand the principles of GUI design and event-driven programming.
• Build interactive applications with user-friendly interfaces.

Topics Covered:

• GUI
• GUI (SQLite and Tkinter)
• Project Work: Project Development using Tkinter

Module-4: Web Application Development Using Django Framework

Objective:

• Develop web applications using the Django framework.
• Understand the MVC (Model-View-Controller) architecture.
• Learn to handle databases, user authentication, and static files.
• Build dynamic and scalable web applications.

Topics Covered:

• Introduction to front end: HTML, CSS, JavaScript
• Introduction to Django: Features of Django, Django web server, Understanding Django environment
• Displaying Hyperlinks – PROJECT
• Creating a website – PROJECT
• Administration panel
• Django Forms Creation
• Using Databases in Django
• Project Work: Project Development using Django

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

Objective:

• Implement image processing techniques using Python.
• Utilize libraries such as OpenCV and PIL for image manipulation.
• Develop applications for real-world image processing tasks.

Topics Covered:

• Introduction to Python libraries for image processing
• Advanced image manipulation and enhancement techniques
• Understanding image histograms
• Image restoration techniques, Edge detection techniques, Feature extraction techniques
• Image segmentation, Thresholding techniques, Watershed segmentation
• Object detection and recognition, template matching, deep learning for image classification and recognition
• Image classification model with TensorFlow, Advanced deep learning models for medical image processing
• Preprocessing, Segmentation and Registration of medical images
• Understanding 3D image processing, image visualization and manipulation, filtering and segmentation
• Image compression technique, JPEG and Wavelet-based compression technique
• Introduction to image steganography, hiding data and Extracting hidden data from images using Python
• Project Work: Project Development

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

Objective:

• Provide skills for developing AI applications that involve natural language

Topics Covered:

• Basic Concept on Natural Language Processing, What is: A Natural Language?, Natural Language Processing?, Language Syntax and Structure, Applications of Natural Language Processing NLTK, Python 3 and the Jupyter Notebook
• Introduction to HPC.
• Sources and Formats 1: “What’s in a Text?”, APIs, social media, Web Scraping
• Building your Corpus (NLP Data Preprocessing), Text cleaning and Tokenization, Removal of special characters, Case conversion, Correcting spellings, Removal of stopwords
• Tokenization, N-grams and Scriptio continua, Stemming and Lemmatization, Synsets and Hypernyms, Tokenizing your Corpus, Introduction to SpaCy.
• POS Tagging and Stop words, Text “Features” and TF-IDF Classification,
• Named Entity Recognition (NER), Sentiment Analysis, Parts of Speech (POS) Tagging, Shallow Parsing/ Chunking, Dependency and Constituency parsing.
• Topic Modelling Basics ,Strengths, Weaknesses, Correlations,
• Document Clustering and Word Vectors, Doc2vec, Word2vec , Advanced Vector Analyses. What is text Classification?, Automated Text Classification, Classification Models.
• Essential concepts of text Similarity, Analysing Term Similarity, Analysing Document Similarity,
• Cluster analysis, Clustering Applications Introduction to Semantic Analysis, WordNet, NER Tagger, Analysis of Semantic Representations.
• Concept on Bangla NLP processing.
• Project Work: Project Development 

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

Objective:

• Equip students with the ability to develop machine learning models using Python.

Topics Covered:

• Overview: Orientation and Course Overview, Basic Concept on  Artificial Intelligence, Machine Artificial Neural Network, Deep Larning and Their Relations, Applications of Deep Learning, Training and Test Data, Confusion Matrix, Overfitting and Underfitting, Hold Out and Cross validation Method, Accuracy, error, Recall and Precision, Data Preprocessing using Python
• Decision Tree
• K-Nearest Neighbour and Support Vector Machine (SVM) 
• Linear and Logistic Regression:
• Naïve Bayes and Bayesian Belief Network
• K-means and Agglomerative Clustering
• Principle Components Analysis (PCA)
• MDP and Q-Learning
• Ensemble Learning:
• Majority Voting, Bagging and Boosting
• Stacking and Random Forest
• Project Work: Project Development

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

Objective:

• Provide advanced knowledge and skills for developing deep learning models.

Topics Covered:

• Orientation and Course Overview, Basic Concept on Data Science, Artificial Intelligence, Machine Artificial Neural Network, Deep Larning and Their Relations, Applications of Deep Learning and Data Preprocessing.
• ANN (Single Layer Perceptron)
• ANN (Multi-Layer Perceptron)
• Convolutional Neural Network (CNN)
• Self-Organizing Map (SIM)
• Restricted Boltzmann Machine (RBM)
• Auto Encoder
• Deep Belief Network (DBN)
• Recurrent Neural Network (RNN)
• Long Short-Term Memory (LSTM):
• Generative Adversarial Network (GAN)
• Project Work: Project Development

Learning Outcomes:

Curriculum