Data Science / Intermediate
Machine Learning Internship (Reviewed Edition)
Company-provided, self-paced internship training from FUTURE FORGE HUB. Four modules with 20 text lessons, runnable examples, self-checked exercises and a practical project; module quizzes and a final assessment record your completion. A suggested 30-day schedule supports earlier completion. Coding practice requires a computer. No videos or project grading are included. A completion certificate records actual enrollment and completion dates. Build and evaluate tabular machine-learning pipelines, prevent leakage, and practice safe model persistence. Python knowledge is required.
Your curriculum
4 modulesComplete the lessons, pass each module quiz, then take the final assessment.
01Module 1: Machine Learning Foundations5 lessons
- What Is Machine Learning?Reading · 40 min
- Types of Machine LearningReading · 60 min
- The Machine Learning WorkflowReading · 60 min
- Data and FeaturesReading · 60 min
- Evaluation MetricsReading · 60 min
02Module 2: Data Preparation5 lessons
- NumPy EssentialsReading · 60 min
- pandas for Tabular DataReading · 60 min
- Cleaning and Missing ValuesReading · 60 min
- Feature Engineering and EncodingReading · 60 min
- Train/Test Split and ScalingReading · 60 min
03Module 3: Supervised Learning5 lessons
- Linear RegressionReading · 60 min
- Logistic RegressionReading · 60 min
- Decision Trees and Random ForestsReading · 60 min
- k-Nearest NeighborsReading · 60 min
- Model Evaluation and Confusion MatrixReading · 60 min
04Module 4: Improvement and Deployment5 lessons
- Cross-Validation and Hyperparameter TuningReading · 60 min
- Pipelines and PreprocessingReading · 60 min
- Overfitting and RegularizationReading · 60 min
- Saving and Loading ModelsReading · 60 min
- Basic Deployment and MonitoringReading · 240 min
Machine Learning Final Assessment
Pass with 70% or higher to complete the program and receive your internship completion certificate.