Data Science & Deep Learning

Learn with Python hands-on examples in 50hours

About the Course

Provides everything one need to know about deep learning with Python that revolutionizing the data science industry.

Course in Brief

Python 3.x (Any Platform) with Pycharm and Jupyter Notebook

NumPy: Mathematical Computing

Pandas: Data Manipulation

Matplotlib, Tableau: Visualization

NLTK, Beautiful soup, PySpark

Machine learning, Natural Language Processing

Computer Vision & Open CV

Prerequisites for this course

Programming language knowledge,

Comfortable with variables (dependent and independent), linear/quadratic equations, graphs of functions, histograms, probability and statistics.

Basic Computer H/W and S/W Knowledge

Desired to Learn
Learners’ must have a system with any 64-bit OS, preferably Windows 10

Course Level: Advanced

Mode of delivery: Online (English)

Course Fee: Rs 19500.00

Instructor: From Industry

Support: Online Text Chat with the Instructor

Certification and Job Assistance

Associated Courses:
Data Science
Python Programming
Machine Learning

Course content

Computation, Manipulation & Visualization

1. Overview
- Overview of Data science
- What is Data Science Different Sectors Using Data Science

2. NumPy: Mathematical Computing
- Introduction to Numpy
- Activity-Sequence
- Creating and Printing an ndarray
- Class and Attributes of ndarray
- Basic Operations
- Activity: Slicing, Copy and Views
- Mathematical Functions of Numpy
- Advance Slicing, Transpose and Searching

3. Pandas: Data Manipulation

- Introduction of Pandas
- Data Types in Pandas
- Understanding Series
- Understanding DataFrame
- View and Select Data Demo
- Missing Values
- Data Operations
- File Read and Write Support

4. Tableau: Data Visualization

5. NLTK

6. Beautiful soup

7. PySpark

Algorithm Implementations

8. Machine Learning with Project Overview and Project Solutions
     - Linear and Logistic Regression
     - Decision Tree
     - Random Forest
     - K-Nearest Neighbour,
     - SVM
     - K-Mean

9. Principal Component Analysis
- Standardize dataset matrix
- covariance matrix for features
- Sort and map eigenvalues with eigenvectors
- Transform the original matrix

10. Natural Language Processing

- Natural Language Processing Theory
- NLP with Python
- NLP Project
- Overview
- NLP Project Solutions

11. Computer Vision & Open CV 
- Image Manipulations
- Image Segmentation
- Object Detection
- Face, People and Car Detection
- Face Analysis
- Machine Learning in Computer Vision
- Motion Analysis & Object Tracking 

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