What will i learn?
- Master AI & ML concepts from beginner to advanced, including NLP and Computer Vision.
- Gain hands-on experience with real-world AI model building and deployment techniques.
- Learn to work with industry-standard tools like TensorFlow, Keras, OpenCV, and NLP frameworks.
- Develop job-ready skills through practical projects aligned with industry requirements.
- Receive industry-recognized certification provided by Udeck Services.
Curriculum for this course
83 Lessons
00:00:00 Hours
MODULE 1: AI & ML Foundations
5 Lessons
00:00:00 Hours
- History, Evolution & Scope of AI/ML 00:00:00
- Key AI Concepts: Machine Learning, Deep Learning, Data Science 00:00:00
- Applications Across Industries: Healthcare, Finance, Retail, E-Commerce 00:00:00
- AI Ethics: Responsible AI, Bias in AI Systems 00:00:00
- Correlation Matrix, Statistical Testing 00:00:00
MODULE 2: Mathematical Foundations for AI & ML
4 Lessons
00:00:00 Hours
- Linear Algebra: Vectors, Matrices, Tensors 00:00:00
- Probability & Statistics: Distributions, Hypothesis Testing 00:00:00
- Calculus: Derivatives, Gradients for Optimization 00:00:00
- Optimization Algorithms: Gradient Descent, Stochastic Gradient 00:00:00
MODULE 3: Advanced Python Programming for AI/ML
4 Lessons
00:00:00 Hours
- Advanced Python Concepts (OOP, Generators, Decorators) 00:00:00
- Data Structures: Lists, Dictionaries, Sets, Tuples 00:00:00
- Error Handling, Debugging, Unit Testing 00:00:00
- Writing Clean, Production-Grade Code 00:00:00
MODULE 4: Libraries for Data Science & Visualization
4 Lessons
00:00:00 Hours
- NumPy: Numerical Computing with Arrays 00:00:00
- Pandas: DataFrames, Series, Data Cleaning 00:00:00
- Matplotlib / Seaborn: Visualization for Data Analysis 00:00:00
- Plotly / Dash: Interactive Visualizations 00:00:00
MODULE 5: Data Preprocessing & Wrangling
4 Lessons
00:00:00 Hours
- Handling Missing, Duplicate, Inconsistent Data 00:00:00
- Scaling, Normalization, Encoding 00:00:00
- Feature Engineering, Feature Selection 00:00:00
- Outlier Detection, Data Balancing (SMOTE, etc.) 00:00:00
MODULE 6: Exploratory Data Analysis (EDA)
2 Lessons
00:00:00 Hours
- Data Insights for Model Selection 00:00:00
- Visualization Storytelling (Dashboards, Reports) 00:00:00
MODULE 7: Supervised Learning (Regression & Classification)
4 Lessons
00:00:00 Hours
- Linear Regression, Polynomial Regression 00:00:00
- Logistic Regression, Decision Trees, Random Forest 00:00:00
- Gradient Boosting: XGBoost, LightGBM, CatBoost 00:00:00
- Model Evaluation Metrics: Confusion Matrix, ROC, AUC 00:00:00
MODULE 8: Unsupervised Learning (Clustering & Association)
3 Lessons
00:00:00 Hours
- K-Means, Hierarchical Clustering, DBSCAN 00:00:00
- Principal Component Analysis (PCA), t-SNE 00:00:00
- Association Rules: Apriori, FP-Growth 00:00:00
MODULE 9: Advanced ML Model Optimization
4 Lessons
00:00:00 Hours
- Hyperparameter Tuning: Grid Search, Random Search, Optuna 00:00:00
- Cross-Validation Techniques (K-Fold, Stratified) 00:00:00
- Bias-Variance Trade-off 00:00:00
- Ensemble Methods (Bagging, Boosting, Stacking) 00:00:00
MODULE 10: Deep Learning Fundamentals
4 Lessons
00:00:00 Hours
- Artificial Neural Networks (ANN) 00:00:00
- Backpropagation, Activation Functions (ReLU, Sigmoid) 00:00:00
- Loss Functions (MSE, Cross-Entropy) 00:00:00
- Optimization (Adam, RMSProp) 00:00:00
MODULE 11: TensorFlow & Keras (Production-Grade DL)
4 Lessons
00:00:00 Hours
- TensorFlow Basics: Tensors, Graphs, Sessions 00:00:00
- Building DL Models: Sequential, Functional APIs 00:00:00
- Transfer Learning: VGG, Inception, ResNet 00:00:00
- TensorBoard Monitoring, Model Saving & Restoration 00:00:00
MODULE 12: Computer Vision (CV) with Deep Learning
4 Lessons
00:00:00 Hours
- Image Classification: CNN Architectures 00:00:00
- Object Detection: YOLO, SSD, Faster-RCNN 00:00:00
- Image Segmentation (U-Net, Mask-RCNN) 00:00:00
- OpenCV for Preprocessing & Real-Time Applications 00:00:00
MODULE 13: Natural Language Processing (NLP)
5 Lessons
00:00:00 Hours
- Text Cleaning: Tokenization, Lemmatization, Stop Words 00:00:00
- Feature Engineering: Bag of Words, TF-IDF 00:00:00
- Word Embeddings: Word2Vec, GloVe, FastText 00:00:00
- Named Entity Recognition (NER), Topic Modeling 00:00:00
- Advanced: Transformers, BERT (Hugging Face Library) 00:00:00
MODULE 14: Reinforcement Learning (RL) Fundamentals
4 Lessons
00:00:00 Hours
- RL Concepts: Agent, Environment, Rewards 00:00:00
- Exploration vs. Exploitation 00:00:00
- Q-Learning, SARSA 00:00:00
- Practical Use Cases: Gaming, Robotics, Finance 00:00:00
MODULE 15: Time Series Analysis
4 Lessons
00:00:00 Hours
- Time Series Components: Trend, Seasonality, Noise 00:00:00
- AR, MA, ARIMA, SARIMA Models 00:00:00
- Forecasting with Facebook Prophet 00:00:00
- LSTM Networks for Sequential Data 00:00:00
MODULE 16: AI Model Deployment & MLOps
4 Lessons
00:00:00 Hours
- Docker Containers, Flask/FastAPI APIs 00:00:00
- TensorFlow Serving, ONNX Models 00:00:00
- Streamlit / Gradio for AI Apps 00:00:00
- CI/CD for AI Pipelines, MLFlow Tracking 00:00:00
MODULE 17: Cloud AI Platforms (AWS, Azure, Google Cloud)
4 Lessons
00:00:00 Hours
- Google AI Tools (Vertex AI, AutoML) 00:00:00
- AWS SageMaker AI Deployment 00:00:00
- Azure AI for NLP, Computer Vision 00:00:00
- Cloud Storage, Security, Scalability 00:00:00
MODULE 18: Industry-Focused Real-Time Projects
7 Lessons
00:00:00 Hours
- Project 1: Healthcare: Disease Detection with CNN 00:00:00
- Project 2: Banking: Fraud Detection using Anomaly Detection 00:00:00
- Project 3: Retail: Customer Churn Prediction 00:00:00
- Project 4: Finance: Stock Market Prediction (LSTM) 00:00:00
- Project 5: E-commerce: AI-Powered Recommendation System 00:00:00
- Project 6: NLP Chatbot for Customer Service 00:00:00
- Project 7: Sentiment Analysis for Social Media Monitoring 00:00:00
MODULE 19: AI Business Applications & Ethics
4 Lessons
00:00:00 Hours
- AI for Business Intelligence 00:00:00
- AI in Marketing, Healthcare, Finance 00:00:00
- Case Studies on Failures & Success 00:00:00
- Ethics, Fairness, and Bias in AI Systems 00:00:00
MODULE 20: Career Preparation, Portfolio & Certification
5 Lessons
00:00:00 Hours
- Resume Building for AI/ML Roles 00:00:00
- GitHub, LinkedIn AI Portfolio Projects 00:00:00
- Mock Interviews: Technical & HR 00:00:00
- Freelancing Guidance, Job Market Insights 00:00:00
- Certification from Udeck Services 00:00:00
Requirements
- A Laptop or Desktop
- Basic Computer Knowledge
- Willingness to Learn and Practice
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Description
Udeck Services offers the most comprehensive Full AI & ML Certification program designed for graduates with a strong computer science background. This 6-7 month advanced training course covers the complete journey from AI & ML fundamentals to real-world AI model deployment.
Learners will master the core pillars of AI: Machine Learning algorithms, Natural Language Processing (NLP), Computer Vision, AI Ethics, and Model Deployment in Production environments. Using industry-standard tools like Python, TensorFlow, Keras, OpenCV, and advanced NLP frameworks, students will build practical skills through hands-on projects and case studies.
This program prepares students for high-demand roles such as AI Engineer, ML Engineer, Data Scientist, and AI Consultant by providing deep practical exposure to both theoretical and applied AI concepts.
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Thu, 17-Jul-2025rahul kumar
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