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 |1| **Intro to Data Science** | 23rd November, 2019 | Applications of Data Science, Tools and Flow |
 |2| **Gradient Descent and related algorithms.** | 7th December, 2019 | Using the assignments that we have provided in Meetup 1, we have to explain gradient descent, and possibly the other such algorithms. The assignment for this meetup will be considering a loss function and randomly initiated the weights and plotting the loss using python.|
 |3| **Regression Techniques.** | 21st December, 2019 | We'll include Linear and Logistic Regression in this session.|
-|4| **Tree-based and Bagging Algorithms.** | 4th January, 2020 | Decision Tree, Random Forest, etc|
-|5| **Boosting Techniques.** | 18th January, 2020 | XGBoost, LightGBM, etc|
-|6| **Intro to Neural Nets.** | 1st February, 2020 | We'll start from linear and logistic regression and complicate it a little to explain Deep Neural Nets. If possible we can explain CNNs and RNNs as well.|
+|4| **Tree-based Algorithms, Bagging and Boosting Techniques** | 4th January, 2020 | Decision Tree, Ensembling, Random Forest, XGBoost, LightGBM etc|
+|5| **Intro to Neural Nets.** | 18th January, 2020 | We'll start from linear and logistic regression and complicate it a little to explain Deep Neural Nets. If possible we can explain CNNs and RNNs as well.|
+|6| **Unsupervised learning Algorithms** | 1st February, 2020 | Clustering, Association Rules, K-Means, DNN-based (Ex:Auto-Encoders) etc|
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