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Statistics Fundamentals
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Python programming
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Machine Learning
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Fundamentals of Deep Learning
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Advanced Deep Learning concepts
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Test Analytics
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Statistics Fundamentals
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Python Programming
- Python is a critical tool for Data Science. In this module participants learn Python programming from basic to advanced level using Jupyter notebooks. Here, participants create, subset and manipulate various data structures.
- Specific libraries like NumPy, Pandas and Matplotlib that are popular for Data Analysis are covered in depth.
- ESSENTIALS OF PYTHON PROGRAMMING
- BASIC DATA STRUCTURES AND FUNCTIONS IN PYTHON
- INTRODUCTION TO
- Numpy Library
- Pandas Library
- Matplotlib and Seaborn Library
- DATA EXPLORATION USING STATISTICS
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Machine Learning
- Machine Learning algorithms are the backbone of Predictive Modelling. This is where the Crux of Data Science lies. The end objective of solving a data science problem is finding the patterns in the data and represent that in the form of a Data model. The algorithms taught in our course cover almost all of the problems data scientists solve on a regular basis.
- Introduction to Supervised and Unsupervised Learning
- Linear Regression with Multiple Variables
- Logistic Regression
- Decision Trees [CART]
- k-Fold Cross Validation
- Bagging and Bootstrapping
- Random Forest
- Gradient Boosting (XGBoost)
- Principal component Analysis
- K-means clustering
- Hierarchical Clustering
- Market Basket Analysis
- KNN
- Support Vector Machine
- Naive Bayes
- Time Series Analysis
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Deep Learning Fundamentals
- In this module, participants will learn the foundations of Deep Learning and understand how to build neural networks. The implementation of the same will be done using Python, TensorFlow and Keras.
- Introduction to Artificial Neural networks with Keras
- From Biological to Artificial Neurons
- Implementing MLPs with Keras
- Fine-Tuning Neural Network Hyperparameters
- Training Deep Neural Networks
- Vanishing/Exploding Gradients Problems
- Reusing Pretrained Layers
- Faster Optimizers
- Avoiding Overfitting Through Regularization
- Summary and Practical Guidelines
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Deep Learning Advanced
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