In the course we will cover all of these topics of data science and machine learning:
- Business Understanding ( Define the business problem)
- Data Mining ( Gather data)
- Data Cleaning (Fix inconsistencies within the data and handle the missing values)
- Data Exploration (Visualize data)
- Feature Engineering ( Select important features)
- Predictive Modelling ( Train machine learning algorithm, evaluate their performance, and use them to make prediction)
- Data visualization (Show your results with interactive charts and graphs)
Here's my skill stack:
PYTHON :
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
FEATURE ENGINEERING:
- Balancing imbalance data
- Outlier and Missing value Treatment
- Standardization of Numeric Values
- Encoding of Categorical Values
- Feature Selection
- Data Visualization
MACHINE LEARNING :
- Statistics, hypothesis testing, t-test, z-test
- Supervised and Unsupervised Learning
- Classification and Regression Algorithms
- Decision tree, Random Forest, SVM, LogReg, Neural network
- Linear Regression, clustering, K means clustering
I teach: SQL, NoSQL, Excel, PoweBI, Python and R.
If you are a college or university student, I can help you in your final project with report writing.