Build a strong foundation in Machine Learning (ML) and learn how computers can identify patterns, learn from data, and make predictions without being explicitly programmed for every individual task. This course takes learners from the fundamentals of machine learning through practical model development, evaluation, and real-world applications.
Students will learn how to prepare datasets, select appropriate machine learning algorithms, train and evaluate models, and understand how machine learning is applied across business, technology, finance, healthcare, marketing, automation, and other industries.
What You Will Learn
- Machine Learning fundamentals
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning concepts
- Regression algorithms
- Classification algorithms
- Clustering techniques
- Decision Trees
- Random Forest
- K-Nearest Neighbors
- Support Vector Machines
- Naive Bayes
- Linear and Logistic Regression
- Feature engineering
- Data preprocessing
- Handling missing and inconsistent data
- Training and testing datasets
- Model evaluation and validation
- Accuracy, precision, recall and F1-score
- Overfitting and underfitting
- Cross-validation
- Model optimization
- Hyperparameter tuning
- Python for Machine Learning
- NumPy and Pandas fundamentals
- Scikit-learn
- Data visualization for ML
- Building practical ML models
- Introduction to Neural Networks
- Machine Learning project workflow
- Deploying and applying ML models
- Real-world Machine Learning use cases
Practical Projects
Learners can work on practical projects such as:
- Customer churn prediction
- House price prediction
- Sales forecasting
- Customer segmentation
- Spam detection
- Recommendation systems
- Classification models
- Predictive analytics projects
Suitable For
This course is suitable for students, aspiring Machine Learning Engineers, Data Scientists, Python developers, IT professionals, data analysts, and anyone interested in developing practical Machine Learning skills.
Course Outcome
By completing the course, learners will understand the complete basic Machine Learning workflow, from collecting and preparing data to training, evaluating, improving, and applying machine learning models to real-world problems.
