Elite Android Developer Program
This 30-day program teaches Android development from basics to advanced topics, including UI components, Kotlin, Firebase integration, and cloud services. Participants will gain hands-on experience with app performance optimization, security, testing, and advanced architecture patterns. The course culminates in a final project to build an enterprise-level Android app, preparing participants for real-world MNC development environments.
- Bestseller
Mentor Sync Program from Navikshaa
- Upcoming batch 1st Feb
- English
Elite Android Developer Program
A course by Prashant
Program teaches Android development from basics to advanced topics, including UI components, Kotlin, Firebase integration, and …
- 1,938
- 98%
₹17,990
What you will learn
- Master the Android development environment with Android Studio and SDK.
- Build your first Android app with essential UI components and layouts.
- Dive deep into Java and object-oriented programming tailored for Android.
- Create dynamic and interactive user interfaces with advanced UI components.
- Understand the Android activity lifecycle and data passing between components.
- Implement Android permissions and best practices for app security.
- Build dynamic lists and handle item interactions with RecyclerView.
- Integrate APIs and manage networking requests for data retrieval.
- Store and manage data effectively using Android’s local databases.
- Work with internal and external storage, including SharedPreferences.
- Handle background tasks and optimize app performance with async operations.
- Get hands-on with Kotlin, including conversion from Java and advanced features.
- Embrace modern development practices with Architecture Components.
- Learn unit and UI testing, ensuring app reliability and stability.
- Build custom views and animations to enhance app UI.
- Utilize Dependency Injection for scalable and modular app design.
- Master advanced RecyclerView techniques for complex data handling.
- Leverage Jetpack Compose for intuitive and modern UI development.
- Integrate with Firebase for cloud storage, authentication, and real-time data syncing.
The course includes :
- 45+ hours live Sessions
- 25 Quizes
- 4 Assessments
- Query Resolution
- 24/7 Professional Support
- No Resume Building Sessions
- No Mock Interviews
- Updated Recorded sessions
- MNC Simulated Project
- Training Completion Certificate
- Letter of Recommendation
- Wipro Credential Certificate
- No Placement Assistance
Course Content
- 45+ Hours Live Sessions
- 29 Quizzes
- 4 Assessments
Day 1: Introduction to Artificial Intelligence
- Overview of AI, its history, and evolution
- Types of AI: Narrow AI vs. General AI
- Applications of AI in business and technology
- Overview of AI, its history, and evolution
- Quiz
Day 2: Core Concepts of Machine Learning
- Introduction to machine learning
- ML vs. AI vs. Deep Learning
- Real-world examples of ML applications
- Quiz
Day 3: Machine Learning Basics and Tools
- Overview of common machine learning libraries: Scikit-learn, TensorFlow, Keras
- Data handling with Pandas and NumPy
- Basic data preprocessing techniques
- Quiz
Day 4: Supervised Learning – Regression Techniques
- Linear regression and multiple linear regression
- Hands-on with real-world datasets (e.g., sales prediction)
- Model evaluation: Mean Squared Error, R²
- Quiz
Day 5: Supervised Learning – Classification Algorithms
- Introduction to classification: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees
- Hands-on with classification tasks (e.g., binary classification)
- Quiz
Assessment 1
- Assessment 1
Day 6: Unsupervised Learning – Clustering
- Introduction to unsupervised learning and clustering
- K-Means Clustering, DBSCAN, Hierarchical Clustering
- Real-world applications: Customer segmentation
- Quiz
Day 7: Model Evaluation and Hyperparameter Tuning
- Evaluating machine learning models
- Cross-validation and overfitting/underfitting
- Hyperparameter tuning using Grid Search and Random Search
- Quiz
Day 8: Support Vector Machines (SVM)
- Introduction to Support Vector Machines
- SVM for classification and regression
- Real-world applications: Text classification, image classification
- Quiz
Day 9: Ensemble Learning Techniques
- Overview of ensemble methods: Random Forest, Bagging
- Hands-on with ensemble learning for improving model accuracy
- Quiz
Day 10: Advanced Machine Learning – Dimensionality Reduction
- Principal Component Analysis (PCA)
- Feature selection and reduction
- Applications: Reducing dimensionality in large datasets (e.g., image or gene data)
- Quiz
Assessment 2
- Assessment 1
Day 11: Introduction to Deep Learning
- What is deep learning? Differences from traditional ML
- Neural networks: Architecture and learning process
- Activation functions, loss functions, and backpropagation
- Quiz
Day 12: Convolutional Neural Networks (CNN)
- Introduction to CNNs and their architecture
- Applications in image classification, object detection
- Hands-on project: Image recognition using CNN
- Quiz
Day 13: Recurrent Neural Networks (RNN)
- Introduction to RNNs and LSTMs (Long Short-Term Memory)
- Applications in time series forecasting, speech recognition
- Hands-on project: Sentiment analysis with RNN
- Quiz
Day 14: Deep Learning Model Evaluation
- Evaluating deep learning models: Overfitting, underfitting, and tuning
- Hyperparameter optimization for deep learning models
- Advanced techniques: Dropout, Batch Normalization, Early Stopping
- Quiz
Day 15: Transfer Learning and Pre-trained Models
- Understanding transfer learning and fine-tuning pre-trained models
- Practical applications using models like ResNet, Inception, and VGG16
- Hands-on: Transfer learning for image classification
- Quiz
Assessment 3
- Assessment 3
Day 16: Natural Language Processing (NLP) Fundamentals
- Introduction to NLP and text preprocessing
- Tokenization, stemming, lemmatization, stop words removal
- Hands-on with Python libraries: NLTK, spaCy
- Quiz
Day 17: Text Classification and Sentiment Analysis
- Introduction to text classification techniques
- Sentiment analysis using deep learning (e.g., LSTM, BERT)
- Hands-on with real-world datasets (social media posts, reviews)
- Quiz
Day 18: Sequence Modeling with RNN and LSTM
- Advanced sequence modeling using RNN and LSTM
- Applications: Time series prediction, stock market forecasting, NLP tasks
- Hands-on project: Stock price prediction with LSTM
- Quiz
Day 19: Reinforcement Learning Basics
- Introduction to reinforcement learning (RL)
- Key concepts: Agents, environments, rewards, policies
- Hands-on with simple RL algorithms (Q-learning)
- Quiz
Day 20: Advanced Deep Learning: GANs (Generative Adversarial Networks)
- Introduction to GANs and how they work
- Applications: Image generation, style transfer, and data augmentation
- Hands-on with GAN-based projects
- Quiz
Assessment 4
- Assessment 4
Day 21: AI Model Deployment
- Introduction to model deployment concepts
- Packaging models with Docker, deploying on cloud platforms (AWS, GCP, Azure)
- API integration for model serving
- Quiz
Day 22: AI for Business Applications
- AI in business: Automation, recommendation systems, and predictive analytics
- Hands-on: Building a recommendation engine using collaborative filtering
- Applications in e-commerce, healthcare, and finance
- Quiz
Day 23: AI in Healthcare
- AI applications in healthcare: Diagnostics, personalized treatment, medical imaging
- Case studies: AI in radiology, drug discovery, and patient monitoring
- Hands-on: Building a simple AI model for medical diagnosis
- Quiz
Day 24: AI in Finance and Risk Management
- AI for fraud detection, credit scoring, and algorithmic trading
- Case studies: AI in financial institutions
- Hands-on: Building a fraud detection model
- Quiz
Day 25: AI in Manufacturing and Automation
- AI for predictive maintenance, robotics, supply chain optimization
- Case studies: Smart factories, automation solutions
- Hands-on: Building an AI-based predictive maintenance system
- Quiz
Day 26: Introduction to AI on the Cloud
- Cloud AI services: AWS, Google Cloud AI, Azure Machine Learning
- Hands-on: Using Google Cloud AI for model deployment
- Benefits and challenges of cloud-based AI solutions
- Quiz
Day 27: AI for Edge Computing
- Introduction to edge AI and its importance for real-time processing
- Case studies: AI in IoT devices, autonomous vehicles
- Hands-on: Deploying a model on a Raspberry Pi
- Quiz
Day 28: AI Ethics and Responsible AI
- Ethical concerns in AI: Bias, fairness, and transparency
- AI governance and regulation in MNCs
- Building ethical AI systems for business applications
- Quiz
Day 29: Capstone Project: Building an Enterprise AI Solution
- Work on a real-world AI project
- Apply concepts learned in the course to solve a business problem
- Quiz