amitsingh2903 / supervised-ml-stanford

Course-I of ML Specialization from Stanford Online and DeepLearning.AI taught on Coursera by Andrew Ng

Throughout the first three weeks of Andrew Ng's Supervised Machine Learning course, I embarked on an immersive journey into the world of Machine Learning (ML). In Week 1, the course commenced with a clear introduction to ML, explaining its goal of building intelligent systems that learn from data. We delved into the two main types of ML, Supervised and Unsupervised Learning, with a primary focus on Supervised Learning. The spotlight was on Logistic Regression, a powerful algorithm for binary classification problems. Andrew Ng's explanation of its mathematical representation and training using Gradient Descent was remarkably intuitive. Week 2 continued with the essential topic of data preprocessing, highlighting the significance of Feature Scaling and its impact on model performance. The introduction of Polynomial Regression opened doors to capturing nonlinear relationships between features and output. Andrew Ng's practical approach and real-world examples enriched my understanding of these concepts.

In Week 3, the course ventured into advanced regression techniques and evaluation metrics. Regularization became a key tool to combat overfitting, with L1 and L2 regularization methods thoroughly explained. The exploration of Classification brought insight into Multiclass Classification and the One-vs-All and One-vs-One approaches. Understanding evaluation metrics, including Precision, Recall, F1 Score, and Confusion Matrix, empowered me to assess models accurately. The introduction of Support Vector Machines (SVM) showcased its power in handling classification and regression tasks with non-linear decision boundaries. Overall, this comprehensive journey equipped me with a solid foundation in supervised ML, inspiring me to embrace more challenges and explore the intriguing realm of Machine Learning further.

In summary, the first three weeks of Andrew Ng's Supervised Machine Learning course were an exhilarating experience. From fundamental concepts to advanced techniques, I learned about Logistic Regression, Gradient Descent, Feature Scaling, Polynomial Regression, Regularization, Multiclass Classification, Evaluation Metrics, and Support Vector Machines. The combination of theoretical knowledge and practical applications, along with Dr. Ng's engaging teaching style, fueled my passion for Machine Learning and equipped me with valuable tools to tackle real-world ML problems. I look forward to continuing this journey, eager to delve deeper into the world of Supervised Machine Learning.