using Python

Artificial Intelligence and Machine Learning with python has become the hot skill in the industry. It is essential for the graduates and professionals to learn and master these skills. One has to be well versed with python, statistics, mathematics and algorithm to become a data scientist.

- Provide minds-on and hands-on training
- Understand Python and its applications
- Understand Data science and how to use python to solve data science problem
- Learn python libraries such as Numpy, Pandas,Matlablib, Scikit learn,Web scraping, OpenCV and NLTK to build
- Learn machine learning algorithmst
- Build sample datascience project to solve real life problems

- Trainees are expected to be well versed with python and its libraries to solve data science problem
- Trainees should be able to independently identify data science problem and build model to solve the problem
- To develop ability to convert algorithm to python code
- Training on Python Data science should enable trainees to solve objective and programming type questions. This would help them to prepare for placements/switch career.

- Data Science Overview
- Data Science
- Data Scientists
- Examples of Data Science
- Python for Data Science

- Introduction to Python and its usage in the industry
- Introduction to Anaconda
- Installation of Anaconda Python Distribution
- Jupyter Notebook Installation
- Jupyter Notebook Introduction
- Variable Assignment
- Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
- Creating, accessing, and slicing tuples
- Creating, accessing, and slicing lists
- Creating, viewing, accessing, and modifying dicts
- Creating and using operations on sets
- Basic Operators: 'in', '+', '*'
- Functions
- Lambda functions
- Object Oriented Programming
- Regular expression
- Database programming
- Sample programs and assignment

- Introduction to Data Visualization
- Processes in Data Science
- Data Wrangling, Data Exploration, and Model Selection
- Exploratory Data Analysis or EDA
- Data Visualization
- Plotting
- Hypothesis Building and Testing

- Introduction to Statistics
- Statistical and Non-Statistical Analysis
- Some Common Terms Used in Statistics
- Data Distribution: Central Tendency, Percentiles, Dispersion
- Histogram
- Bell Curve
- Hypothesis Testing
- Chi-Square Test
- Correlation Matrix
- Inferential Statistics

- NumPy Overview
- Properties, Purpose, and Types of ndarray
- Class and Attributes of ndarray Object
- Basic Operations: Concept and Examples
- Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Copy and Views
- Universal Functions (ufunc)
- Shape Manipulation
- Broadcasting
- Linear Algebra

- Data Structures
- Series
- DataFrame
- Missing Values
- Data Operations
- Data Standardization
- Pandas File Read and Write Support
- SQL Operation

- Introduction to Data Visualization
- Python Libraries
- Matplotlib Features:
- Plots
- Line Properties Plot with (x, y)
- Controlling Line Patterns and Colors
- Set Axis, Labels, and Legend Properties
- Alpha and Annotation
- Multiple Plots
- Subplots
- Types of Plots and Seaborn

- Introduction to Machine Learning
- Machine Learning Approach
- How Supervised and Unsupervised Learning Models Work
- Scikit-Learn
- Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Tree
- Support Vector Machine (SVM)
- Naive Bayes
- K Nearest Neighbour (KNN)
- K-Means
- Random Forest
- Dimensionality Reduction Algorithms
- Model Persistence
- Model Evaluation - Metric Functions

- Web Scraping
- Common Data/Page Formats on The Web
- Beautiful Soup for web scraping
- Scrape data from few web sites

- NLP Overview
- NLP Approach for Text Data
- NLP Environment Setup
- NLP Sentence analysis
- NLP Applications
- Major NLP Libraries
- Scikit-Learn Approach
- Scikit - Learn Approach Built - in Modules
- Scikit - Learn Approach Feature Extraction
- Bag of Words
- Extraction Considerations
- Sentimental analysis

- Sample project on datascience
- Assessment

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