Duration
5 days
Course Overview
This intensive 5-day course provides a comprehensive introduction to data analysis and machine learning. Participants will learn to preprocess data, explore data sets, apply machine learning algorithms, and interpret results. The course combines theoretical concepts with hands-on practical applications using popular tools and libraries.
Session 1: Overview of Data Analysis and Machine Learning
- Introduction to Data Analysis
- Definition and importance
- Key concepts in data analysis
- Introduction to Machine Learning
- Definition and types (supervised, unsupervised, reinforcement)
- Common applications of machine learning
Session 2: Setting Up the Environment
- Introduction to Python and relevant libraries (NumPy, pandas, scikit-learn, Matplotlib)
- Installation and setup instructions
- Basic Python programming concepts
Session 3: Data Collection and Preprocessing
- Data Collection Techniques
- Types of data (structured vs. unstructured)
- Data sources and APIs
- Data Cleaning
- Handling missing values
- Removing duplicates
- Data Transformation
- Encoding categorical variables
- Feature scaling
Session 1: Exploratory Data Analysis (EDA)
- Data Visualization Techniques
- Plotting with Matplotlib and Seaborn
- Histograms, scatter plots, box plots
- Statistical Analysis
- Descriptive statistics
- Correlation analysis
- Identifying Patterns and Outliers
- Techniques to detect and handle outliers
Session 2: Introduction to Supervised Learning Algorithms
- Linear Regression
- Decision Trees
- k-Nearest Neighbors (k-NN)
- Hands-on exercises and implementation
Session 3: Model Evaluation and Selection
- Model Evaluation Metrics
- Accuracy, Precision, Recall, F1 Score
- Confusion Matrix
- Cross-Validation
- k-Fold Cross-Validation
- Leave-One-Out Cross-Validation
- Hyperparameter Tuning
- Grid Search
- Random Search
Session 1: Advanced Machine Learning Algorithms
- Support Vector Machines (SVM)
- Random Forests
- Gradient Boosting Machines (GBM)
- Neural Networks (Introduction)
- Hands-on exercises and implementation
Session 2: Feature Engineering
- Feature Selection
- Importance of feature selection
- Techniques: Filter methods, Wrapper methods, Embedded methods
- Feature Extraction
- Techniques: PCA, LDA
- Feature Creation
Session 3: Time Series Analysis
- Introduction to Time Series Data
- Components of time series (trend, seasonality, noise)
- Time Series Forecasting Models
- ARIMA
- Exponential Smoothing
- Evaluation Metrics for Time Series
Session 1: Introduction to NLP
- Text Preprocessing
- Tokenization, stemming, lemmatization
- Text Representation
- Bag of Words
- TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Basic NLP Models
- Sentiment Analysis
- Topic Modeling
Session 2: Practical Session
- Hands-on exercises integrating data analysis and machine learning techniques
- Project work
Session 1: Model Deployment and Production
- Introduction to Model Deployment
- Importance and techniques
- Deployment Tools and Frameworks
- Flask, Docker
- Monitoring and Maintenance of Models
- Model performance tracking
- Retraining models
Session 2: Capstone Project
- End-to-end project encompassing data collection, preprocessing, EDA, model building, evaluation, and deployment
- Group work and presentations
Session 3: Course Review and Q&A
- Summary of key concepts
- Best practices in data analysis and machine learning
- Q&A session
- Future Learning Paths and Resources
Programme scope, delivery arrangements and suitability are confirmed during enquiry.