Intelligent assistants like Alexa and Siri, image searches that found the topic of a photo, and self-driving cars – these intelligent systems used machine learning to develop their expertise. In this module, I learned about various machine learning techniques but concentrated on deep neural learning. I learned about the underlying theory and got hands-on experience creating, training, evaluating, and using machine learning systems. I also examined how these technologies were used and misused and what that meant for our societies and communities.

 

Learning Outcomes

  • Deepened understanding of statistical techniques beyond introductory level for practical data analysis.
  • Modeled variation in data using commonly used models and investigated their properties.
  • Applied estimation, confidence intervals, hypothesis tests, and regression in statistical analysis.
  • Communicated statistical analyses clearly to both statistical and non-statistical audiences.

Feedback on my assingments

The feedback across the three assignments underscores a commendable start with strengths in understanding and applying data analysis concepts. However, it identifies specific areas for improvement, such as choosing appropriate data visualization methods, understanding statistical distributions, and performing accurate calculations of probabilities, proportions, and statistical measures.

Reflections

The feedback highlights a progression in understanding, with each assignment revealing areas where deeper comprehension and precision in application are needed. Particularly, the feedback emphasizes the importance of correctly identifying and applying statistical distributions and the need for precision in calculating and interpreting statistical measures.

Professional Skills Matrix learnt and Action Plan

 

Skills Gained or Enhanced:

  • Analytical Skills: Understanding and application of statistical concepts and data visualization techniques.
  • Attention to Detail: Precision in statistical calculations and correct identification of data types and distributions.
  • Critical Thinking: Ability to analyze and describe data accurately, using appropriate statistical methods and visualizations.

Action Plan:

  • Deepen Understanding of Data Types and Visualizations: Review materials on different data types and their suitable visualizations. Practice by categorizing various data sets and selecting the most appropriate visual representation for each.
  • Master Statistical Distributions: Focus on understanding and applying different statistical distributions, especially Bernoulli and Poisson distributions as noted in the feedback. Use online resources, textbooks, and exercises to reinforce learning.
  • Enhance Calculation Accuracy: Practice calculating proportions, expected values, variances, and probabilities with a variety of data sets. Pay particular attention to the correct application of formulas and the interpretation of results.
  • Engage in Tutor Group Sessions: Actively participate in tutor group sessions, especially those covering complex topics like maximum likelihood, to clarify doubts and learn from discussions.
  • Utilize Feedback for Revision: Revisit feedback for each assignment and address the specific areas of improvement. Consider creating a checklist of common mistakes to avoid in future assignments.