History of DL – SchoolingIT

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Data Science
History of DL

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Lessons

Descriptive Statistics/EDA in Python

Mean/Median/Mode/IQR/Kurtosis/Skew
Univariate Plots
Histograms
Density Plots
Box and Whisker Plots
Multivariate Plots
Correlation Matrix Plot
Scatter Plot Matrix

Laying the foundation

Basic Statistics
Basics of Probability
Probability Distribution
Hypothesis Testing
p value
Z and T Test
One tail vs Two tail test

Pre-Modelling Techniques in Python

Standardize/Normalize data
Feature Selection
Feature Importance
Splitting into Train/Test data
K-Fold Cross Validation
LOOC Validation

Model Evaluation Metrics in Python

Classification Accuracy
Log Loss
ROC/AUC
Confusion Matrix
F1/Recall/Precision
MAE/MSE/RMSE
Underfitting and Overfitting
Regularization Techniques

Predictive Modelling in Python*

Intiution of Machine Learning Model
Linear Regression
Logistic Regression
Classification Modelling
K means and Hierarchical Clustering
Decision Trees
Ensemble Models
Bagging Algorithms
Random Forest
Bagging Classifier/Regressor
ExtraTrees
Boosting Algorithms
AdaBoost
XGBoost

Deep Learning in Python

History of DL
ML vs DL vs AI
Fundamentals of Neural Networks
Activation Layers
Depth of Network
Backpropagation
CNN
Covolution, Pooling, Dropout, Batch Normalization
Data Augmentation, Transfer Learning
Basics of TF/Keras
Basics of NLP using DL
Word2Vec, Embeddings,CNN for text
RNN and Sequence Network
LSTM
Chatbot

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SOA
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