Data Science with python course
Duration:
180 HRS
Subjects:
Data Science with python
- Python Basics
- Data Structures in Python
- Control & Loop Statements in Python
- Functions & Classes in Python
- Working with Data
- Opps concept etc
- Data Acquisition (Import & Export)
- Selection and Filtering Sorting & Summarizing
- Descriptive Statistics
- Combining and Merging Data Frames
- Removing Duplicates
- Discretization and Binning
- String Manipulation
- Indexing
- Data Visualization & EDA
- Understand Time Series Data
- Visualizing Time Series Components
- Exponential Smoothing
- Holt’s Model
- Holt-Winter’s Model
- ARIMA
- ARCH & GARCH
- K-Means Clustering
- Principal Component Analysis (PCA)
- Scree Plot
- One-Eigen Value Criterion
- Factor Analysis
- Machine Learning Modelling Flow
- How to treat Data in ML
- Parametric & Non-parametric ML Algorithm
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting & Underfitting
- Optimization Techniques
- Scikit-Learn Library
- Linear Regression
- Linear Regression with Stochastic Gradient Descent, Batch GD
- Optimizing Learning Rate
- Momentum
- Logistic Regression with Stochastic Gradient Descent, Batch GD
- Optimizing Learning Rate
- Momentum
- Understanding KNN
- Voronoi Tessellation
- Choosing K
- Distance Metrics – Euclideam, Manhattan, Chebyshev
- Fundamental Concepts of Ensemble
- Hyper-Parameters
- What is SVM?
- When to use SVM?
- Understanding Hyperplane
- What is Support Vector?
- Understanding Langragian Multiplier, Karush Kuhn Tucker Conditions
- SVM Kernels – Radial Basis Function, Gaussian Kernel, Linear Kernel
- Optimizing the C Parameter
- Regularization
- Etc……………………………..
- Project….
Course
