MACHINE LEARNING ENGINEERING BOOTCAMP
Embark on a journey into the fascinating realm of Machine Learning. Our course delves into the principles and applications of machine learning algorithms, preparing you to tackle real-world problems and contribute to the advancements in artificial intelligence.
- Duration: 3 Months
- Cost: £1,000
- Start: January/May/September
Program
Month 1: Introduction to Machine Learning
Week 1: Introduction to Machine Learning
- Understanding the basics of machine learning
- Different types of machine learning: supervised, unsupervised, and reinforcement learning
- Overview of popular machine learning frameworks
Week 2: Python for Machine Learning
- Essential Python programming for ML
- NumPy, Pandas, and Matplotlib for data manipulation and visualization
- Introduction to Jupyter notebooks
Week 3: Exploratory Data Analysis (EDA)
- Data preprocessing techniques
- Descriptive statistics
- Data visualization with Seaborn and Matplotlib
Week 4: Supervised Learning - Regression
- Linear regression
- Polynomial regression
- Evaluation metrics for regression models
Month 2: Advanced Supervised Learning and Unsupervised Learning
Week 5: Supervised Learning - Classification
- Logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
Week 6: Unsupervised Learning
- Clustering algorithms (K-means, hierarchical, DBSCAN)
- Dimensionality reduction (PCA)
- Anomaly detection
Week 7: Model Evaluation and Hyperparameter Tuning
- Cross-validation
- Grid search and random search for hyperparameter tuning
- Model evaluation and selection
Week 8: Natural Language Processing (NLP)
- Text preprocessing
- Sentiment analysis
- Introduction to basic NLP techniques
Month 3: Deep Learning and Real-world Applications
Week 9: Introduction to Deep Learning
- Neural networks basics
- Activation functions, loss functions, and optimization algorithms
- TensorFlow and Keras introduction
Week 10: Convolutional Neural Networks (CNNs)
- Image classification with CNNs
- Transfer learning using pre-trained models
Week 11: Recurrent Neural Networks (RNNs) and LSTM
- Sequence-to-sequence models
- Time series analysis with RNNs
Week 12: Capstone Project and Deployment
- Real-world project implementation
- Deployment strategies for machine learning models
- Ethical considerations and best practices
Delivery Method
- Instructor-led lectures
- Hands-on coding exercises and projects
- Collaborative coding sessions
- Guest lectures from industry experts
- Access to online resources and forums for continuous learning
Assessment
- Regular quizzes and coding assignments
- Mid-term and final projects
- Participation in group discussions and code reviews
Certification
Participants will receive a Machine Learning Bootcamp Certification upon successful completion of the program.
Prerequisites
Basic understanding of programming (preferably Python) and familiarity with basic mathematical concepts such as algebra and statistics.
Why Choose Us?
