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My learning journey in Machine Learning With Python

My learning journey in Machine Learning with Python has been an exciting and fulfilling experience, equipping me with the necessary knowledge and skills to develop and implement machine learning models to solve complex problems.

At the beginning of my journey, I started by learning the fundamentals of Python programming language, which is the backbone of machine learning. This included learning the syntax, data structures, and control flow statements in Python. I also learned how to use popular libraries such as NumPy, Pandas, and Matplotlib, which are essential for data analysis and visualization in machine learning.

As I progressed further, I began to explore the various techniques and algorithms used in machine learning. This included learning about supervised and unsupervised learning, regression analysis, classification, clustering, and natural language processing. I also gained knowledge of various model evaluation metrics such as accuracy, precision, recall, and F1-score.

One of the most valuable lessons I learned during my journey was the importance of data preparation and feature engineering in machine learning. I learned how to preprocess data by cleaning, scaling, and transforming it into a format that machine learning models can understand. I also learned how to select relevant features and engineer new features to improve the performance of the models.

Finally, I had the opportunity to apply my knowledge and skills in a real-world setting, through various projects and challenges. This allowed me to gain practical experience in developing machine learning models for real-world applications, such as predicting customer churn, fraud detection, and sentiment analysis.

Week 1: Brief Prerequisite Reviews


  • Statistics: Before diving into machine learning, it’s important to have a good understanding of basic statistical concepts such as probability, distributions, hypothesis testing, and regression analysis. These concepts are essential for evaluating the performance of machine learning models and interpreting their results.
  • Linear Algebra: Linear algebra provides the mathematical foundation for many machine learning algorithms. It’s important to have a solid understanding of matrix algebra, vector calculus, and eigenvalues and eigenvectors. These concepts are used to represent and manipulate data in high-dimensional spaces and to perform operations such as matrix multiplication, matrix inversion, and eigenvalue decomposition.
  • Programming Basics: While not strictly necessary, having a strong foundation in programming basics is important for learning machine learning. This includes learning a programming language such as Python or R, understanding control structures such as loops and conditionals, and understanding functions and data structures such as arrays and lists. This will help you write code to implement machine learning algorithms and evaluate their performance.

I found some problems quite interesting:
A univariate Gaussian or normal distributions can be completely determined by its mean and variance.
Gaussian distributions can be applied to a large numbers of problems because of the central limit theorem (CLT). The CLT posits that when a large number of independent and identically distributed ((i.i.d.) random variables are added, the cumulative distribution function (cdf) of their sum is approximated by the cdf of a normal distribution.
Recall the probability density function of the univariate Gaussian with mean mu and variance N(mu, delta^2)

Probability review: PDF of Gaussian distribution


In practice, it is not often that you will need to work directly with the probability density function (pdf) of Gaussian variables. Nonetheless, we will make sure we know how to manipulate the (pdf) in the next two problems.

Week 2: Linear Classifiers and Generalizations


  • Linear Classifiers: Linear classifiers are an important class of machine learning algorithms that are widely used for classification tasks. They work by dividing the input space into different regions using a linear boundary, such as a hyperplane in high-dimensional space. Some of the popular linear classifiers include logistic regression, linear SVMs, and perceptron.
  • Regularization: Regularization is an important technique used to prevent overfitting in machine learning models. It involves adding a penalty term to the loss function to discourage the model from overfitting the training data. Some of the popular regularization techniques include L1 regularization (Lasso) and L2 regularization (Ridge regression).
  • Model Evaluation: Evaluating the performance of machine learning models is an essential part of the learning process. It’s important to understand different evaluation metrics such as accuracy, precision, recall, and F1-score, as well as how to use techniques such as cross-validation and learning curves to assess the generalization performance of the model. Additionally, understanding the bias-variance tradeoff and how it affects the performance of the model is important for selecting appropriate models and tuning hyperparameters.

In this problem, we will try to understand the convergence of perceptron algorithm and its relation to the ordering of the training samples for the following simple example.

Working out Perceptron Algorithm

Overall, my learning journey in Machine Learning with Python has been an enriching experience, providing me with the necessary knowledge and skills to develop and implement machine learning models to solve complex problems. I look forward to applying these skills in my future endeavors and contributing to the continued advancement of machine learning.

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