Zameer Ahmad AI scientist
No reviews yet

Highly skilled and experienced professional specializing in Python programming, Jupyter Notebook, and a range of advanced technologies. Adept at utilizing Microsoft Office, Overleaf, VS Code, and Android Studio for diverse project needs. Demonstrated expertise in machine learning, deep learning, and image processing, with a proven track record of delivering high-quality results in both academic and industry settings.
Key Skills:
Scientific Software: Overleaf, Anaconda, VS Code, Android Studio
Programming Languages: Python, PHP, JavaScript, C++
Computer Skills: Experienced in Windows systems

Certified Python Developer
Machine Learning Specialization
Deep Learning Certification
Image Recognition System: Developed a deep learning-based image recognition system achieving 95% accuracy.
Predictive Maintenance: Implemented machine learning algorithms to predict equipment failures, reducing downtime by 30%.

Subjects

  • Machine Learning Expert

  • Image Processing Expert

  • Artificial intelligence Expert

  • NLP basics Intermediate

  • Deep Learning Projects in Python Expert

Experience

  • Software Developer (Jun, 2021–Jun, 2022) at 128 Technologies
    As a backend Laravel and flutter developer

Education

  • Software Engineering (Feb, 2019–Feb, 2023) from The university of lahore, Lahore–scored 3.14/4.00

Fee details

    RM10–15/hour (US$2.25–3.37/hour)

    Fees will be vary from project to project and complexity of work

Courses offered

  • Machine Learning

    • US$200
    • Duration: 2 Months
    • Delivery mode: Online
    • Group size: 5
    • Instruction language: English
    • Certificate provided: No
    Students can learn each topic with secondary and primary datasets:
    1. Introduction to Machine Learning
    Overview and Importance
    Definition of Machine Learning
    Real-world applications
    Importance in today's technological landscape
    Types of Machine Learning
    Supervised Learning
    Unsupervised Learning
    Reinforcement Learning
    2. Data Preprocessing
    Data Cleaning
    Handling missing values
    Removing duplicates
    Data imputation
    Data Transformation
    Encoding categorical variables
    Normalization and standardization
    Feature Scaling
    Min-Max Scaling
    Z-score normalization
    3. Supervised Learning
    Regression
    Linear Regression
    Polynomial Regression
    Classification
    Logistic Regression
    Decision Trees
    Random Forests
    Support Vector Machines
    4. Unsupervised Learning
    Clustering
    K-means Clustering
    Hierarchical Clustering
    DBSCAN
    HDBSCAN
    Dimensionality Reduction
    Principal Component Analysis (PCA)
    Linear Discriminant Analysis (LDA)
    t-Distributed Stochastic Neighbor Embedding (t-SNE)
    5. Model Evaluation and Selection
    Cross-Validation
    k-Fold Cross-Validation
    Leave-One-Out Cross-Validation
    Bias-Variance Tradeoff
    Understanding bias and variance
    Strategies to balance bias and variance
    Performance Metrics
    Accuracy
    Precision, Recall, and F1 Score
    ROC Curve and AUC
    6. Ensemble Methods
    Bagging and Boosting
    Concept and benefits
    Popular Ensemble Algorithms
    AdaBoost
    Gradient Boosting
    XGBoost
    7. Neural Networks and Deep Learning
    Introduction to Neural Networks
    Basic architecture
    Activation functions
    Convolutional Neural Networks (CNNs)
    Architecture and use cases
    Recurrent Neural Networks (RNNs)
    Sequence modeling
    Transfer Learning
    Pre-trained models and fine-tuning
    8. Natural Language Processing (NLP)
    Text Preprocessing
    Tokenization
    Stop words removal
    Stemming and Lemmatization
    Core NLP Tasks
    Sentiment Analysis
    Topic Modeling
    Sequence-to-Sequence Models
    9. Time Series Analysis
    Time Series Forecasting
    Moving averages
    Exponential smoothing
    Advanced Models
    ARIMA, SARIMA
    Long Short-Term Memory Networks (LSTMs)
    10. Reinforcement Learning
    Foundations
    Markov Decision Processes
    Q-Learning
    Deep Reinforcement Learning
    Deep Q-Networks
    11. Advanced Topics
    Generative Adversarial Networks (GANs)
    Architecture and applications
    Explainable AI (XAI)
    Importance and methods
    AutoML
    Automated machine learning tools
    Federated Learning
    Distributed model training
    12. Practical Applications
    Industry Use Cases
    Finance, Healthcare, Agriculture, etc.
    Model Deployment
    Techniques and tools
    Deployment strategies
    Ethical Considerations in AI
    Fairness and bias
    Privacy and security
    13. Tools and Libraries
    Python Libraries
    Scikit-learn
    TensorFlow
    Keras
    PyTorch
    Data Visualization
    Matplotlib
    Seaborn
    Model Deployment
    Flask
    Docker
    Kubernetes

  • Deep learning

    • US$200
    • Duration: 2 Months
    • Delivery mode: Online
    • Group size: 4
    • Instruction language: English
    • Certificate provided: No
    Students can learn each topic with secondary and primary datasets:
    Deep Learning Course Outline
    1. Introduction to Deep Learning
    Overview and Importance
    Definition and significance of deep learning
    Historical context and advancements
    Applications in various fields (computer vision, NLP, etc.)
    2. Neural Networks Basics
    Perceptron and Multilayer Perceptrons (MLP)
    Structure and functioning of a perceptron
    Concept of multi-layer perceptrons
    Activation functions (Sigmoid, ReLU, Tanh, Softmax)
    Training Neural Networks
    Forward propagation
    Backpropagation and gradient descent
    Loss functions (MSE, Cross-Entropy)
    3. Deep Neural Networks (DNNs)
    Architecture of DNNs
    Layers (input, hidden, output)
    Depth and width of networks
    Regularization Techniques
    L1 and L2 regularization
    Dropout
    Batch normalization
    4. Convolutional Neural Networks (CNNs)
    Core Concepts
    Convolution operation
    Pooling layers
    CNN Architectures
    LeNet, AlexNet, VGG, Inception, ResNet
    Applications of CNNs
    Image classification
    Object detection
    Image segmentation
    5. Recurrent Neural Networks (RNNs)
    Fundamentals
    Sequential data processing
    Vanishing and exploding gradient problems
    Advanced RNN Architectures
    Long Short-Term Memory (LSTM)
    Gated Recurrent Unit (GRU)
    Applications of RNNs
    Language modeling
    Machine translation
    Speech recognition
    6. Advanced Deep Learning Techniques
    Generative Models
    Generative Adversarial Networks (GANs)
    Architecture and training process
    Variations (DCGAN, WGAN)
    Variational Autoencoders (VAEs)
    Latent space representation
    Applications in data generation
    Attention Mechanisms and Transformers
    Attention concept
    Transformer architecture
    Applications in NLP (e.g., BERT, GPT)
    7. Transfer Learning
    Concept and Benefits
    Pre-trained models
    Fine-tuning techniques
    Popular Pre-trained Models
    VGG, ResNet, Inception, BERT, GPT
    8. Deep Learning for Natural Language Processing (NLP)
    Text Representation
    Word embeddings (Word2Vec, GloVe)
    Contextual embeddings (ELMo, BERT)
    Sequence Modeling
    Encoder-Decoder models
    Sequence-to-Sequence models with attention
    9. Reinforcement Learning (RL) in Deep Learning
    Fundamentals of RL
    Markov Decision Processes (MDPs)
    Policy and value functions
    Deep Reinforcement Learning
    Deep Q-Networks (DQN)
    Policy Gradient methods
    Actor-Critic methods
    10. Model Evaluation and Optimization
    Evaluation Metrics
    Accuracy, Precision, Recall, F1 Score
    Confusion matrix, ROC-AUC
    Optimization Techniques
    Learning rate schedules
    Gradient clipping
    Advanced optimizers (Adam, RMSprop)
    11. Practical Considerations
    Model Deployment
    Tools and frameworks (TensorFlow Serving, ONNX, TensorRT)
    Serving models in production environments
    Ethical and Societal Implications
    Bias and fairness
    Privacy concerns
    AI safety and regulations
    12. Tools and Libraries
    Deep Learning Frameworks
    TensorFlow and Keras
    PyTorch
    Data Handling and Preprocessing
    NumPy, Pandas
    OpenCV for image processing
    Visualization Tools
    Matplotlib, Seaborn
    TensorBoard

  • Paython Programing

    • US$100
    • Duration: 1 Month
    • Delivery mode: Online
    • Group size: 6 - 10
    • Instruction language: English
    • Certificate provided: No
    Students can learn each topic in real-time examples:
    Python Programming Course Outline
    1. Introduction to Python
    Overview and History
    Origin of Python
    Key features and benefits
    Python in various domains (web development, data science, automation, etc.)
    Setting Up the Environment
    Installing Python
    Introduction to IDEs (PyCharm, VS Code, Jupyter Notebook)
    2. Python Basics
    Syntax and Semantics
    Writing and running Python scripts
    Python indentation and code structure
    Basic Data Types
    Numbers (integers, floats)
    Strings
    Booleans
    Variables and Assignments
    Variable naming conventions
    Dynamic typing
    3. Control Structures
    Conditional Statements
    if, elif, else
    Loops
    for loop
    while loop
    Loop control statements (break, continue, pass)
    4. Functions and Modules
    Defining Functions
    Function syntax
    Parameters and arguments
    Return values
    Scope and Lifetime of Variables
    Local vs. global scope
    Modules and Packages
    Importing modules
    Standard library modules
    Creating and using packages
    5. Data Structures
    Lists
    Creating and accessing lists
    List methods and operations
    Tuples
    Characteristics of tuples
    Tuple operations
    Dictionaries
    Key-value pairs
    Dictionary methods and operations
    Sets
    Set properties and operations
    Set methods
    6. File Handling
    Reading and Writing Files
    Open, read, write, and close files
    Working with text and binary files
    File Methods and Operations
    File modes
    Context managers
    7. Exception Handling
    Errors and Exceptions
    Common error types
    Handling exceptions using try, except, else, finally
    Raising Exceptions
    Custom exceptions
    8. Object-Oriented Programming (OOP)
    Classes and Objects
    Defining classes
    Creating objects
    Class Attributes and Methods
    Instance vs. class variables
    Method definitions
    Inheritance
    Base and derived classes
    Method overriding
    Encapsulation and Polymorphism
    Private and protected members
    Polymorphic behavior
    9. Advanced Topics
    Iterators and Generators
    Understanding iterators
    Creating generators using yield
    Decorators
    Function decorators
    Class decorators
    Lambda Functions
    Syntax and usage
    Applications in functional programming
    10. Working with Libraries and Frameworks
    NumPy
    Array operations
    Mathematical functions
    Pandas
    DataFrames and Series
    Data manipulation and analysis
    Matplotlib and Seaborn
    Data visualization basics
    Creating plots and charts
    11. Web Development with Python
    Introduction to Flask and Django
    Setting up a Flask application
    Basic Django project setup
    Building Web Applications
    Routes and views
    Templates and static files
    12. Data Science with Python
    Introduction to Data Science
    Overview of data science workflow
    Key Python libraries for data science
    Data Analysis and Visualization
    Data cleaning and preprocessing
    Exploratory data analysis (EDA)
    Machine Learning Basics
    Introduction to Scikit-learn
    Building simple models
    13. Automation and Scripting
    Automating Tasks
    Using Python for scripting
    Automating web tasks with Selenium
    Working with APIs
    Making HTTP requests
    Parsing JSON and XML
    14. Testing and Debugging
    Unit Testing
    Writing test cases with unittest
    Running and interpreting test results
    Debugging Techniques
    Using the pdb debugger
    Debugging in IDEs
    15. Best Practices and Version Control
    Code Style and PEP 8
    Writing readable and maintainable code
    Version Control with Git
    Basic Git commands
    Working with repositories

  • Letax & Overleaf

    • US$120
    • Duration: 1 Month
    • Delivery mode: Online
    • Group size: 3
    • Instruction language: English
    • Certificate provided: No
    Students will be able to learn each topic with proper examples:
    LaTeX & Overleaf Course Outline:
    1. Introduction to LaTeX
    Overview and Importance
    History and significance of LaTeX
    Benefits of using LaTeX for document preparation
    Comparison with traditional word processors
    Getting Started with LaTeX
    Installation and setup (TeX distributions)
    Introduction to LaTeX editors (TeXShop, TeXworks, etc.)
    Short Project:
    Create a simple document with a title, author, and date.
    2. Basic Document Structure
    Document Classes and Packages
    Common document classes (article, report, book, etc.)
    Importing and using packages
    Basic Commands and Syntax
    Text formatting (bold, italic, underline)
    Special characters and symbols
    Short Project:
    Write a formatted document with sections, subsections, and basic text formatting.
    3. Formatting Text
    Sections and Paragraphs
    Creating sections, subsections, and paragraphs
    Managing spacing and line breaks
    Lists and Enumerations
    Creating ordered and unordered lists
    Nested lists and custom markers
    Short Project:
    Create a document with various text formatting elements, including lists and enumerations.
    4. Mathematical Typesetting
    Basic Mathematics
    Inline and display math modes
    Common mathematical symbols and expressions
    Advanced Mathematics
    Matrices, integrals, and derivatives
    Aligning equations and multi-line equations
    Short Project:
    Write a document containing various mathematical equations and expressions.
    5. Figures and Tables
    Inserting Figures
    Including images and graphics
    Positioning and resizing images
    Creating Tables
    Basic table syntax
    Advanced table formatting (multi-column, multi-row)
    Short Project:
    Create a document with properly formatted tables and figures.
    6. Cross-Referencing and Citations
    Cross-Referencing
    Labels and references (sections, figures, tables)
    Hyperlinks and bookmarks
    Bibliographies and Citations
    Using BibTeX for references
    Citation styles and managing references
    Short Project:
    Write a document with cross-references and a bibliography.
    7. Customizing Layout and Design
    Page Layout
    Margins, headers, and footers
    Page numbering and custom styles
    Custom Commands and Environments
    Defining new commands
    Creating custom environments
    Short Project:
    Customize the layout of a document with headers, footers, and custom commands.
    8. Overleaf for Collaborative Writing
    Introduction to Overleaf
    Overview of Overleaf platform
    Creating and managing projects
    Collaboration Features
    Real-time collaboration
    Version control and track changes
    Short Project:
    Set up a collaborative document on Overleaf and invite collaborators.
    9. Advanced Document Preparation
    Presentations with Beamer
    Basics of creating a Beamer presentation
    Themes and transitions
    Thesis and Dissertation Writing
    Structuring a large document
    Managing chapters, appendices, and references
    Short Project:
    Create a Beamer presentation with multiple slides and themes.
    10. Practical Considerations
    Templates and Customization
    Using and modifying LaTeX templates
    Best practices for template management
    Troubleshooting and Debugging
    Common errors and warnings
    Tips for debugging LaTeX documents
    Short Project:
    Use a template to create a complex document, such as a resume or an academic paper.
    11. Integrating LaTeX with Other Tools
    LaTeX and Git
    Version control for LaTeX documents
    Collaborative writing with Git and Overleaf
    LaTeX and Other Software
    Integrating LaTeX with Python (e.g., for generating plots)
    Using LaTeX with reference managers (Zotero, Mendeley)
    Short Project:
    Integrate a LaTeX document with Git for version control and collaborate with a team.

Reviews

No reviews yet. Be the first one to review this tutor.