| Course Name | Duration | Classes | Total Fee | Mode of Training | Class Timing |
|---|---|---|---|---|---|
|
Machine Learning
Course |
2 Months |
40 |
60,000 PKR |
Online & Face-to-Face |
To be decided mutually with students |
Machine Learning Training Certification Course
Technocation provides an excellent faculty and qualified developers as there is a remarkable prospect in this field. One can make his/her Career with the help of both Machine Learning Training and establish an identity and get guidance in Rawalpindi.
Therefore, we aim to shape inspiring students with in-depth training to meet the requirements of the IT industry and build substantial grounds in Machine Learning Training by exhibiting students with various projects. Technocation also bestows the Best Machine Learning Training Course in Rawalpindi, Islamabad.
We guide people from every background to change their lives via our career-oriented short-term courses in Rawalpindi. Our evening and online course primarily focus on school, college, university students, and full/part-time employees.
Advantages of Learning Machine Learning
- High demand for machine learning professionals.
- Solves complex data-driven challenges.
- Reduces manual effort through automation.
Enables informed decision-making.
Machine Learning Training Certification Course Outline
Here’s a comprehensive outline for an Advanced Level Machine Learning course:
Module 1:Advanced Topics in Supervised Learning
- Advanced Regression Models: Ridge Regression, Lasso, Elastic Net, and Neural Networks for Regression.
- Classification Techniques: Support Vector Machines (SVM), Decision Trees, Random Forest, Gradient Boosting, and XGBoost.
- Deep Learning for Supervised Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.
Module 2:Unsupervised Learning
- Dimensionality Reduction: PCA, t-SNE, UMAP.
- Clustering Techniques: K-Means, DBSCAN, Hierarchical Clustering, and Affinity Propagation.
- Generative Models: Variational Autoencoders (VAEs), GANs (Generative Adversarial Networks).
- Advanced RL Techniques: Q-Learning, Policy Gradient, Deep Q-Networks (DQN), and Actor-Critic methods.
Module 3:Natural Language Processing (NLP)
- Transformer Models: BERT, GPT, and other fine-tuned models.
- Sequence Models: Language Models, Sentiment Analysis, Named Entity Recognition (NER), and Text Summarization.
- Advanced Image Processing: Object Detection, Semantic Segmentation, Image Generation, and Anomaly Detection.
- Applications: Facial Recognition, Object Tracking, and Video Analytics.
Module 4:Feature Engineering and Feature Selection:
- Advanced Techniques for Feature Extraction and Dimensionality Reduction.
- Techniques for Handling Missing Data, Categorical Data Encoding, and Data Augmentation.
- Cross-Validation, Hyperparameter Tuning, and Ensemble Methods.
- Model Explainability and Interpretability Techniques: SHAP, LIME, and Anchors.
Module 5:Real-World Applications
- Industry-Specific Applications: Healthcare, Finance, E-commerce, and IoT.
- Case studies and Projects involving advanced model deployment, scaling, and maintenance.
- Fairness, Accountability, and Transparency (FAT) in Machine Learning Models.
- Legal and ethical considerations in deploying AI solutions.
Module 6: Advanced Tools & Technologies
- Distributed Machine Learning, Cloud-Based ML Solutions, and AutoML Platforms.
- Deep Dive into frameworks like TensorFlow, PyTorch, and Scikit-learn.
- Strong understanding of basic machine learning concepts.
- Proficiency in Python, mathematics, and statistical analysis.
Module 7:Advanced Regression Models
- Ridge Regression: Regularization to deal with multicollinearity.
- Lasso Regression: Feature selection by penalizing coefficients of less relevant features.
- Elastic Net: Combines Ridge and Lasso for better model flexibility.
- Neural Networks for Regression: Building Deep Learning models with multi-layer perceptrons (MLPs) and using techniques like Dropout, Batch Normalization, and Attention Mechanisms
Module 8:Advanced Classification Techniques
- Support Vector Machines (SVM):
- Kernel methods: Polynomial, RBF, Sigmoid.
- Hyperparameter tuning: C, Gamma, and kernel types.
- Decision Trees & Random Forest:
- Pruning, ensemble methods, and hyperparameter tuning.
- Gradient Boosting:
- XGBoost, LightGBM, and CatBoost.
- Techniques for handling class imbalance and outliers.
Module 9:Advanced Supervised Learning
- Experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn is recommended.
- Deepen understanding of machine learning algorithms and techniques.
- Explore advanced concepts in artificial intelligence and data science.
- Develop expertise in building complex machine learning models and systems.
Experience and Inspiring Trainers:
Real-time Practice and Projects:
Overall, machine learning equips individuals and organizations with the tools to solve real-world problems, innovate, and stay competitive in an increasingly data-driven world.
Prerequisites for Machine Learning Course:
Programming
Basic Python or R knowledge.Mathematics
Linear algebra, calculus, statistics.Data Handling
Familiarity with data manipulation tools.Algorithms
Understanding of basic machine learning concepts.Statistics
Basic statistical concepts like probability.Tools
Experience with libraries like NumPy, Pandas.Problem Solving
Ability to apply ML concepts to real problems.
Certificate:
Course Material:
Support and Careers Advice:
“Our Student Success is Our Mission”.