Android Application Development Fundamentals For Beginners & Intermediate

From 10 C$ /h

In this comprehensive class, we delve into the essential foundations of Android app development, covering both theoretical concepts and practical implementations. Throughout the course, students will gain proficiency in a wide range of topics, from object-oriented programming principles to the creation of sophisticated application user interfaces. Here's a breakdown of what we'll cover:

1. Basics of Android App Development:

Introduction to the Android platform and its ecosystem.
Understanding the Android development environment, including Android Studio and the Android SDK.
Exploring the components of an Android application, such as activities, fragments, intents, and layouts.
Hands-on exercises to build simple Android apps from scratch.

2. Object-Oriented Concepts and Standard Design Patterns:

Explanation of core object-oriented programming (OOP) concepts such as inheritance, polymorphism, encapsulation, and abstraction.
Introduction to common design patterns like Singleton, Factory, Observer, and MVC (Model-View-Controller).
Practical examples and discussions on when and how to apply design patterns in Android app development.

3. Writing Code Using Architecture Design Patterns:

Deep dive into modern architecture design patterns such as MVVM (Model-View-ViewModel), MVP (Model-View-Presenter), and Clean Architecture.
Hands-on coding sessions to implement these patterns in Android projects.
Best practices for structuring Android codebase for scalability, maintainability, and testability.

4. Understanding Large and Complex Code Bases:

Techniques for navigating and understanding large Android codebases.
Strategies for keeping code clean, modular, and maintainable.
Code refactoring exercises and discussions on code quality metrics and tools.
5. Open-Source Contributions and Project-Based Learning:

Introduction to open-source Android projects and communities.
Guidance on contributing to open-source projects and leveraging them for learning.
Project-based assignments to apply learned concepts and techniques in real-world scenarios.

6. Mentoring for Self-Projects and Guidance Provided:

One-on-one mentoring sessions to provide personalized guidance and support for self-initiated projects.
Feedback and code reviews to help students improve their coding skills and project implementations.
Assistance in overcoming challenges and roadblocks encountered during project development.

By the end of this class, students will not only have a solid understanding of Android app development fundamentals but also possess the skills and knowledge required to tackle complex Android projects with confidence. Whether you're a beginner looking to start your journey in Android development or an experienced developer aiming to level up your skills, this class is designed to empower you with the expertise needed to succeed in the dynamic world of Android app development.

Extra information

learn and understand basic to advanced concepts of android application development.

Location

Online from India

About Me

Hello, I'm Mohammed Fahim, a Senior Android Developer with over six years of experience, currently leading projects at Tagit RFID Solutions. I bring a strong background in architecting and developing Android applications, managing cross-functional teams, and ensuring the delivery of high-quality products.

Skills:
Proficient in Java and Kotlin, Android UI/UX design, and architectural patterns such as MVP and MVVM. I'm well-versed in collaboration tools like Git, JIRA, and understand the Software development lifecycle.

In summary, my extensive experience, technical proficiency, and commitment to continuous learning position me as a valuable contributor to Android development.

Education

I hold a Master's degree in Information Technology from the University of Mumbai, achieving a CGPA of 7.7. My coursework covered Advanced Computer Networks, Cloud Architecture, Artificial Intelligence, and Software Engineering. Additionally, I earned a Bachelor's degree with a CGPA of 7.4, specializing in Java, Data Structures & Algorithms, and Database Systems.

Experience / Qualifications

In my current role, I lead a team of developers, contributing to the architecture and development of the Android applications. I specialize in Java, Kotlin, and frameworks like Android, Flutter and IOS, with Additional hands-on experience in integrating RFID scanners and BLE hardware.

Age

Adults (18-64 years old)

Student level

Beginner

Intermediate

Advanced

Duration

90 minutes

The class is taught in

English

Skills

Java

Android

Computer programming

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

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Associate professor provides support courses in electrical engineering

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Video lessons: Clear explanations and practical exercises.
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A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

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