Tasks and Duties
Task Objective
This week you will establish a comprehensive project plan for a machine learning initiative using Python. The aim is to define and structure the project's scope, identify the key challenges and opportunities, and develop a strategic blueprint for a potential machine learning experiment. Your plan will be documented in a DOC file and submitted as your final deliverable.
Expected Deliverables
- A DOC file containing a detailed project plan.
- A clear definition of the machine learning problem to be tackled.
- An outline of the project strategy including objectives, timelines, and milestones.
- A risk assessment and contingency planning section.
Key Steps
- Introduction and Problem Definition: Begin by selecting a hypothetical or publicly known machine learning challenge. Define the problem context and the importance of solving it.
- Strategic Outline: Develop a strategy that includes research topics, resource planning, programming environment setup, and identification of relevant Python libraries and frameworks.
- Timeline and Milestones: Propose a detailed timeline with weekly milestones over the course of the project. Explain what each phase will accomplish.
- Risk and Mitigation Analysis: Identify potential challenges and propose workable solutions. Outline backup strategies.
Evaluation Criteria
Your submission will be evaluated based on clarity, depth, feasibility, and thoroughness of the strategic plan. Extra credit will be provided for innovative approaches and well-considered mitigation strategies. This task is designed to take approximately 30-35 hours. Ensure that your DOC file is well-organized, free of ambiguities, and clearly structured.
Task Objective
The focus of this week's assignment is on data handling, preprocessing, and exploratory analysis using Python. Although no specific dataset is provided, you are encouraged to reference and discuss techniques applicable to publicly available datasets. You will simulate the data preprocessing phase of a machine learning lifecycle and document your methods, decisions, and outcomes in a DOC file.
Expected Deliverables
- A detailed DOC file including your data preprocessing plan.
- Step-by-step explanations on how you would clean, transform, and explore a dataset.
- Visualization ideas and techniques for exploratory data analysis (EDA).
- An explanation of feature engineering steps.
Key Steps
- Data Collection Strategy: Describe how you would identify and select a dataset from public domain resources. Discuss potential sources and selection criteria.
- Preprocessing Techniques: Detail all necessary cleaning steps such as handling missing values, outlier detection, normalization, scaling, and encoding categorical variables.
- Exploratory Data Analysis (EDA): Outline methods for visualizing data trends using Python libraries (e.g., Matplotlib, Seaborn) and provide mock examples of summary statistics and graphs.
- Feature Engineering: Explain strategies for generating new features or reducing dimensionality.
Evaluation Criteria
Your work will be assessed based on the clarity of your explanation, the depth of your methodological approach, and the practical applicability of your preprocessing plan. The DOC file should be detailed, logically organized, and reflect an understanding of core data preprocessing concepts. Allocate around 30-35 hours to complete this task.
Task Objective
This week is dedicated to the model development phase. You will devise a hypothetical framework for building a machine learning model using Python, describe the process of model selection, training, and evaluation, and discuss potential pitfalls and improvements. The document, submitted as a DOC file, should reflect a deep understanding of model-building concepts and provide a detailed guide that could be implemented step-by-step.
Expected Deliverables
- A DOC file containing your complete model development plan.
- A detailed explanation of algorithm selection appropriate for the defined problem.
- An overview of training processes, hyperparameter tuning, and evaluation strategies.
- A discussion of model validation techniques including cross-validation and performance metrics.
Key Steps
- Model Selection: Discuss pros and cons of various machine learning algorithms suited for your chosen problem scenario. Justify your selection.
- Training Strategy: Outline a step-by-step training plan detailing data splitting, cross-validation, hyperparameter tuning, and iterative improvements.
- Evaluation and Metrics: Define which performance metrics (accuracy, precision, recall, F1 score, etc.) are applicable and why. Consider implications of overfitting and underfitting.
- Error Analysis: Propose a systematic method for identifying model weaknesses and suggest remedial measures.
Evaluation Criteria
You will be assessed on the technical robustness of your proposed model development strategy and clarity in articulating model evaluation methodologies. Your submission should be a well-structured DOC file that can theoretically guide implementation, spending roughly 30-35 hours on this task.
Task Objective
The final task focuses on synthesizing all stages of the machine learning process to produce a comprehensive report. This report should present findings, discuss lessons learned throughout the planning, data preprocessing, and modeling phases, and provide actionable insights and recommendations. The goal is to translate technical details into a coherent narrative that can be easily understood by both technical and non-technical audiences. Please prepare your submission as a DOC file.
Expected Deliverables
- A DOC file that acts as a final report summarizing the entire project lifecycle.
- A clear executive summary highlighting key findings and insights.
- A section describing the methodology followed during planning, data preprocessing, and model development.
- A discussion on evaluation results and potential improvements.
- Recommendations for implementing the machine learning solution in a real-world scenario.
Key Steps
- Executive Summary: Craft a concise overview that highlights the objectives, main actions taken, and key outcomes.
- Detailed Discussion: Provide a thorough explanation of each phase of the project, emphasizing decision logic and technical methods used.
- Evaluation: Present the success metrics, interpret the results, and discuss any challenges faced during the project.
- Recommendations and Future Work: Offer actionable insights based on your findings and suggest directions for future exploration or immediate application.
Evaluation Criteria
Your final report will be evaluated on its clarity, coherence, and professionalism. Judges will be looking for a well-organized document that presents complex technical information in an accessible manner. The submission should demonstrate your ability to critically evaluate each phase of the project and provide insightful recommendations. Plan to spend between 30 to 35 hours on this task to ensure depth and completeness.