Data Science with Dr. Wasim

Data Science with Dr. Wasim is an initiative by Dr. Muhammad Wasim, Assistant Professor at University of Management and Technology, (Sialkot Campus). The objective of this channel is to make machine learning easy for beginners. The students often face challenges when learning this subject. We provide practical code examples and cover both theoretical as well as practical aspects using popular machine learning library. Moreover, the concepts that the students come across in research literature are also covered.

Linkedin Profile: https://www.linkedin.com/in/dr-m-wasim/

88- Lab 39 - Clustering and Choosing the K Value
88- Lab 39 - Clustering and Choosing the K Value
87- Choosing the Value of K
87- Choosing the Value of K
86- Random Initialization Trap
86- Random Initialization Trap
85- K-means Clustering
85- K-means Clustering
84- Unsupervised Learning
84- Unsupervised Learning
83- Lab 38- Multiclass Classification - 2
83- Lab 38- Multiclass Classification - 2
82- Lab 37- Multiclass Classification - 1
82- Lab 37- Multiclass Classification - 1
81 - Lab 36 - Dataset and DataLoader Classes
81 - Lab 36 - Dataset and DataLoader Classes
80- Lab 35- NN for Classification - 2
80- Lab 35- NN for Classification - 2
79- Lab 34- NN for Classification - 1
79- Lab 34- NN for Classification - 1
78- Lab 33- Defining and Training a Neural Network
78- Lab 33- Defining and Training a Neural Network
77 - Lab 32 Pytorch: Tensors
77 - Lab 32 Pytorch: Tensors
76- Neural Networks - Final Remarks
76- Neural Networks - Final Remarks
75 - How do Neural Networks Learn
75 - How do Neural Networks Learn
74- Neural Networks Intuition - 2
74- Neural Networks Intuition - 2
73- Neural Network Intuition - 1
73- Neural Network Intuition - 1
72- Lab 31- SVM Demo - 3
72- Lab 31- SVM Demo - 3
71- Lab 30- SVM Demo - 2
71- Lab 30- SVM Demo - 2
70- Lab 29- SVM Demo - 1
70- Lab 29- SVM Demo - 1
69 - SVM: Final Remarks
69 - SVM: Final Remarks
68- SVM: The Kernel Trick
68- SVM: The Kernel Trick
67 - Support Vector Machine Intuition
67 - Support Vector Machine Intuition
66- Lab 28 - ROC Curve, AUC Score
66- Lab 28 - ROC Curve, AUC Score
65 - Lab 27 - Logistic Regression Demo
65 - Lab 27 - Logistic Regression Demo
64 - Lab 26 - Lasso Regression (L1 Regularization)
64 - Lab 26 - Lasso Regression (L1 Regularization)
63 - Lab 25 - Ridge Regression (L2 Regularization)
63 - Lab 25 - Ridge Regression (L2 Regularization)
62 - ROC Curve - AUC Score
62 - ROC Curve - AUC Score
61 - Sensitivity and Specificity
61 - Sensitivity and Specificity
60- Multiclass Classification
60- Multiclass Classification
59 - Logistic Regression Cost and Overfitting
59 - Logistic Regression Cost and Overfitting