Data Science
Data Science & Machine Learning
Course Highlights
- Introduction to Data Science
- Python with Data Science
- Matplotlib Module
- Scipy
- Scikit
- Machine Learning
- Data Science & Machine Learning
- understanding Machine Learning
- Theano
- Trensorflow
- Importance
-
Artificial intelligence
Data Science & Machine Learning Course Details
1) Matplotlib Module
- Introducation
- Environment Setup
- Anaconda Distribution
- Juypter Notebook
- Pyplot API
- Simple Plot
- PyLab Module
- Axes Class
- Figure Class
- Multiplots
- Subplots() Function
- Subplot2grid() Function
- Grids
- Formatting Axes
- Twin Axes
- Bar Plot
- Histogram
- Pie Chart
- Scatter Plot
- Contour Plot
- Quiver Plot
- Box Plot
- Violin Plot
- 3-dimensional Plotting
- 3D Contour Plot
- 3D Wireframe Plot
- 3D Surface Plot
- Working With Text
- Mathematical Expressions
- Working with Images
- Transforms.
2) Scipy
- Introducation
- Environment Setup
- Basic Functionality
- Cluster
- Constants
- FFT pack
- Integrate
- Interpolate
- Input and Output
- Linalg
- Ndimage
- Optimize
- Stats
- CSGraph
- Spatial
- ODR
- Special Package
3) Scikit
- Introducation
- Modelling Process
- Data Representation
- Estimator API
- Conventions
- Linear Modeling
- Extended Linear Modeling
- Grandient Descent
- Support Vector Machine
- Anomaly Detection
- K-MearestNeighbours
- KNN Learning
- Classification with Naïve Bayes
- Decision Trees
- Rendomized Decision Trees
- Boosting Methods
- Clustering Methods
- Clustering Performance Evaluation
- Dimensionality Reduction using PCA
-
4) Theano
- Introducation
- Installation
- A Trivial Theano Expression
- Expression for matrix Multiplication
- Computational Graph
- Data types
- Variables
- Shared Variables
- Functions
-
7) Trensorflow
- Introducation
- Installation
- Understanding Artificial Intelligence
- Mathematical Foundations
- Machine Learning Learning& Deep Learning
- TensorFlow Basics
- Convolutional Neural Networks
- Recurrent Neual Networks
- TensorBoard Visualization
- TensorFlow- WordEmbedding
- Single Layer Perceptron
- Linear Regression
- TFLearn and its installation
- CNN and RNN Difference
- Keras
- Distributed Computing
- Exporting, Multi-Layer Perceptron Learing
- Hidden Layers of Perceptron
- Optimizers
- XOR Implementation
- Gradient Descent Optimization
- Forming Graphs
- Image Recongnition using TensorFlow