Python for Machine Learning
This course provides a comprehensive introduction to Machine Learning (ML) using Python, covering data preprocessing, model training, evaluation, and deployment.
- Course fee
- Contact us
- Duration
- 5 days
- Group rate
- Ask an advisor
Course overview
This course provides a comprehensive introduction to Machine Learning (ML) using Python, covering data preprocessing, model training, evaluation, and deployment. Participants will gain hands-on experience with key ML libraries like Scikit-learn, TensorFlow, Keras, and Pandas, enabling them to build and deploy machine learning models efficiently.
Training methodology
Hands-on Labs – Practical coding exercises and real-world ML projects
Live Demonstrations – Step-by-step walkthroughs of ML model building
Case Studies & Best Practices – Industry use cases for ML
Collaborative Learning – Group projects and peer discussions
Capstone Project – Developing an end-to-end ML solution
Target competencies
- Python Programming for Machine Learning
- Data Preprocessing & Feature Engineering
- Supervised & Unsupervised Learning Techniques
- Model Evaluation & Hyperparameter Tuning
- Neural Networks & Deep Learning with TensorFlow/Keras
- Deploying ML Models to Production
Course objectives
By the end of the course, participants will be able to:
- Apply Python for Machine Learning tasks
- Prepare and preprocess datasets for ML modeling
- Train and evaluate ML models using Scikit-learn
- Optimize models using feature selection and hyperparameter tuning
- Implement Neural Networks and Deep Learning models
- Deploy ML models into production environments
Course outline
- Overview of Machine Learning Concepts
- Python’s Role in ML – Why Python?
- Setting Up the ML Development Environment (Jupyter, VS Code, Anaconda)
- Introduction to Key Libraries (NumPy, Pandas, Scikit-learn, TensorFlow, Keras)
- Understanding Datasets & Data Types
- Data Cleaning: Handling Missing Values, Outliers, and Duplicates
- Feature Engineering: Encoding Categorical Variables, Scaling, and Normalization
- Dimensionality Reduction Techniques (PCA, LDA)
- Visualizing Data using Matplotlib & Seaborn
- Identifying Trends, Correlations, and Distributions
- Feature Selection & Importance Analysis
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- Gradient Boosting Algorithms (XGBoost, CatBoost, LightGBM)
- Evaluating Model Performance (Accuracy, Precision, Recall, F1-Score, AUC-ROC)
- Understanding Unsupervised Learning
- K-Means Clustering & Hierarchical Clustering
- Principal Component Analysis (PCA) for Dimensionality Reduction
- Association Rule Mining (Apriori, FP-Growth)
- Basics of Neural Networks & Deep Learning
- Implementing a Simple Neural Network with TensorFlow/Keras
- Hyperparameter Tuning & Optimization
- Introduction to Convolutional Neural Networks (CNNs) & Recurrent Neural Networks (RNNs)
- Cross-Validation Techniques (K-Fold, Stratified K-Fold)
- Hyperparameter Tuning using GridSearchCV & RandomizedSearchCV
- Bias-Variance Tradeoff & Overfitting Prevention
- Saving & Loading Models (Pickle, Joblib)
- Deploying Models with Flask & FastAPI
- Using Docker & Kubernetes for ML Model Deployment
- Introduction to ML Model Monitoring & A/B Testing
- Fraud Detection in Banking
- Customer Churn Prediction
- Recommendation Systems
- Sentiment Analysis & NLP Applications
- Time Series Forecasting
Morning and evening classes are available. CompuBase Training Center, Abu Dhabi.
