In this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started. Here are the items we'll cover in this course
The Data Science Process
Python for Data Science
NumPy for Numerical Computation
Pandas for Data Manipulation
Matplotlib for Visualization
Seaborn for Beautiful Visuals
Plotly for Interactive Visuals
Introduction to Machine Learning
Dask for Big Data
Deep Learning & Next Steps
For the machine learning section here are some items we'll cover :
What you’ll learn
- Python for Data Science
- The Data Science Process
- NumPy for Numerical Computation
- Pandas for Data Manipulation
- Matplotlib for Visualization
- Seaborn for Beautiful Visuals
- Plotly for Interactive Visuals
- Introduction to Machine Learning
- Dask for Big Data
- LightGBM
- XGBoost
- CatBoost
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Deep Learning using Keras and TensorFlow
- Artificial Neural Networks
- How Artificial Neural Networks Work
- How Artificial Neural Networks Learn
- Loss Functions used in Artificial Neural Networks
- Activation Functions used in Artificial Neural Networks
- Cost Functions used in Artificial Neural Networks
- Optimizer Functions used in Artificial Neural Networks
- What Backpropagation is
- Different Types of Gradient Descent
- How to Choose an Activation Function
- Preparing your Data for Deep Learning Models
- Monitoring Loss Functions
- Monitoring Model Metrics
- Use of CallBacks in Deep Learning
- Fighting overfitting in TensorFlow
- Convolutional Neural Networks
- Natural Language Processing
- Support Vector Machines
- KNearest Neighbors
- T-Test
- Chi-square Test
- K-Means Clustering
- Principal Component Analysis
- Flask
Goals
- Get acquainted with Python for Data Science
- Understand the Data Science Process
- Perform Numerical Computation with NumPy
- Manipulate Data using Pandas
- Visualize using Matplotlib
- Build interactive visuals with Plotly
- Perform Statistical Analysis
- Build beautiful visuals using Seaborn
- Implement Machine Learning Models
- Load in Big Data using Dask
- Handle Imbalanced Data
- Understand the Intuition Behind Popular Machine Learning Algorithms
- Implement Cross-Validation to improve model performance
- Search for the best model parameters using Grid Search CV
- Use experimental algorithms from Scikit-Learn
- Solve Time Series Problems using Prophet
- Predict Price of a Commodity using Linear Regression
- Host a Machine Learning Model on Heroku
- Build classification and regression models using LightGBM
- Implement classification and regression models using XGBoost
- Build classification and regression models using CatBoost
- Classify data using Logistic Regression
- Build models using Decision Trees & Random Forests
- Perform customer segmentation using KMeans Clustering
- Solve problems using Support Vector Machines
- ...and much more
Prerequisites
- A great sense of curiosity!
