Course Learning Path
- 1. Introduction to Python
- 2. Python Data Science Toolbox
- 3. Data Preparation in Python
- 4. Data Manipulation using Pandas
- 5. Statistics essentials in Python
- 6. Python Environment Setup and Essentials
- 7. Supervised Learning using Scikit
- 8. Unsupervised Learning
- 9. Tree-Based Models in Python
- 10. Deep Learning in Python
- 11. Deep Learning using PyTorch
- 12. Natural Language Processing using Python
- 13. Python Integration with Hadoop MapReduce and Spark
- Python Essentials – Lists, Functions and Packages and NumPy
- Visualization – Matplotlib and Seaborn
- Dictionaries and Pandas
- Loops, Control Flow and Filtering
- Creating new functions
- Lambda functions and error handling
- List comprehensions and generators
- Importing Data in Python – Flat Files, API or Web
- Tidying, Combining and Cleaning Data for Analysis
- Data Ingestion
- Exploratory Data Analysis
- Time Series Data Analysis
- Data Transformation
- Indexing and Reshaping Data
- Rearranging and Grouping Data
- Concatenating and Merging Data
- EDA using Graphical Methods
- EDA using Quantitative Methods
- Discrete and Continuous Variables
- Parameter Estimation
- Confidence Intervals
- Hypothesis Testing
- Conda Essentials
- Installing Packages
- Working with environments
- Classification
- Regression
- Model Tuning
- Data Preprocessing – Missing Data Handling, Data Imputation, Centering and Scaling
- Clustering – K-means, Hierarchical Clustering and t-SNE
- Dimension Reduction – Principal Component Analysis
- Classification and Regression Trees (CART)
- Random Forests
- Boosting
- Model Tuning
- Bias-Variance Tradeoff – Understanding the concept of overfitting and under-fitting
- Introduction to Deep Learning and Neural Networks
- Optimizing Neural Network Model using Back Propagation
- Using Keras to build deep learning models
- Tuning Keras models
- Introduction to PyTorch
- Artificial Neural Networks
- Convolutional Neural Networks
- Introduction to NLP
- Model Building using NLP Libraries – Scikit
- Fine tuning using Grid Search
- Hadoop Core Components
- Python Integration with HDFS using Hadoop Streaming
- Python Integration with Spark using PySpark
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