54 - Domain 3 Hands On Attachment files.html
54 - Domain-3-Modeling.zip
55 - Introduction to Domain 3 Modelling
56 - Introduction to Machine Learning
57 - Types of Machine Learning
58 - Linear Regression Evaluation Functions
59 - Regularization and Assumptions of Linear Regression
60 - Logistic Regression
61 - Gradient Descent
62 - Logistic Regression Implementation and EDA
63 - Evaluation Metrics for Classification
64 - Decision Tree Algorithms
65 - Loss Functions of Decision Trees
66 - Decision Tree Algorithm Implementation
67 - Overfit Vs Underfit Kfold Cross validation
68 - Hyperparameter Optimization Techniques
69 - Quick Checkin on the Syllabus
70 - KNN Algorithm
71 - SVM Algorithm
72 - Ensemble Learning Voting Classifier
73 - Ensemble Learning Bagging Classifier Random Forest
74 - Ensemble Learning Boosting Adabost and Gradient Boost
75 - Emsemble Learning XGBoost
76 - Clustering Kmeans
77 - Clustering Hierarchial Clustering
78 - Clustering DBScan
79 - Time Series Analysis
80 - ARIMA Hands On
81 - Reccommendation Amazon Personalize
82 - Introduction to Deep Learning
83 - Introduction to Tensorflow Create first Neural Network
84 - Intuition of Deep Learning Training
85 - Activation Function
86 - Architecture of Neural Networks
87 - Deep Learning Model Training Epochs Batch Size
88 - Hyperparameter Tuning in Deep Learning
89 - Vanshing Exploding Gradients Initializations Regularizations
90 - Introduction to Convolutional Neural Networks
91 - Implementation of CNN on CatDog Dataset
92 - Transfer Learning for Computer Vision
93 - Feed Forward Neural Network Challenges
94 - RNN Types of Architecture
95 - LSTM Architecture
96 - Attention Mechanism
97 - Transfer Learning for Natural Language Data
98 - Transformer Architecture Overview
