Overview of Python

The Companies using Python

Different Applications where Python is used

Discuss Python Scripts on UNIX/Windows

Values, Types, Variables

Operands and Expressions

Conditional Statements

Loops

Command Line Arguments

Writing to the screen

Creating “Hello World” code

Variables

Demonstrating Conditional Statements

Demonstrating Loops

Fundamentals of Python programming

Python files I/O Functions

Numbers

Strings and related operations

Tuples and related operations

Lists and related operations

Dictionaries and related operations

Sets and related operations

Tuple - properties, related operations, compared with a list

List - properties, related operations

Dictionary - properties, related operations

Set - properties, related operations

File Operations using Python

Working with data types of Python

Functions

Function Parameters

Global Variables

Variable Scope and Returning Values

Lambda Functions

Object-Oriented Concepts

Standard Libraries

Modules Used in Python

The Import Statements

Module Search Path

Package Installation Ways

Errors and Exception Handling

Handling Multiple Exceptions

Functions - Syntax, Arguments, Keyword Arguments, Return Values

Lambda - Features, Syntax, Options, Compared with the Functions

Sorting - Sequences, Dictionaries, Limitations of Sorting

Errors and Exceptions - Types of Issues, Remediation

Packages and Module - Modules, Import Options, sys Path

Error and Exception management in Python

Working with functions in Python

NumPy - arrays

Operations on arrays

Indexing slicing and iterating

Reading and writing arrays on files

Pandas - data structures & index operations

Reading and Writing data from Excel/CSV formats into Pandas

matplotlib library

Grids, axes, plots

Markers, colours, fonts and styling

Types of plots - bar graphs, pie charts, histograms

Contour plots

NumPy library- Creating NumPy array, operations performed on NumPy array

Pandas library- Creating series and dataframes, Importing and exporting data

Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

Probability Distributions in Python

Python for Data Visualization

Basic Functionalities of a data object

Merging of Data objects

Concatenation of data objects

Types of Joins on data objects

Exploring a Dataset

Analysing a dataset

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()

GroupBy operations

Aggregation

Concatenation

Merging

Joining

Python in Data Manipulation

Python Revision (numpy, Pandas, scikit learn, matplotlib)

What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Linear regression

Gradient descent

Linear Regression – Boston Dataset

Machine Learning concepts

Machine Learning types

Linear Regression Implementation

What are Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Perfect Decision Tree

Confusion Matrix

What is Random Forest?

Implementation of Logistic regression

Decision tree

Random forest

Supervised Learning concepts

Implementing different types of Supervised Learning algorithms

Evaluating model output

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

LDA

PCA

Scaling

Implementing Dimensionality Reduction Technique

What is Naïve Bayes?

How Naïve Bayes works?

Implementing Naïve Bayes Classifier

What is Support Vector Machine?

Illustrate how Support Vector Machine works?

Hyperparameter Optimization

Grid Search vs Random Search

Implementation of Support Vector Machine for Classification

Implementation of Naïve Bayes, SVM

Supervised Learning concepts

Implementing different types of Supervised Learning algorithms

Evaluating model output

What is Clustering & its Use Cases?

What is K-means Clustering?

How does K-means algorithm work?

How to do optimal clustering

What is C-means Clustering?

What is Hierarchical Clustering?

How Hierarchical Clustering works?

Implementing K-means Clustering

Implementing Hierarchical Clustering

Unsupervised Learning

Implementation of Clustering – various types

What are Association Rules?

Association Rule Parameters

Calculating Association Rule Parameters

Recommendation Engines

How does Recommendation Engines work?

Collaborative Filtering

Content-Based Filtering

Apriori Algorithm

Market Basket Analysis

Data Mining using python

Recommender Systems using python

What is Reinforcement Learning

Why Reinforcement Learning

Elements of Reinforcement Learning

Exploration vs Exploitation dilemma

Epsilon Greedy Algorithm

Markov Decision Process (MDP)

Q values and V values

Q – Learning

α values

Calculating Reward

Discounted Reward

Calculating Optimal quantities

Implementing Q Learning

Setting up an Optimal Action

Implement Reinforcement Learning using python

Developing Q Learning model in python

What is Time Series Analysis?

Importance of TSA

Components of TSA

White Noise

AR model

MA model

ARMA model

ARIMA model

Stationarity

ACF & PACF

Checking Stationarity

Converting a non-stationary data to stationary

Implementing Dickey-Fuller Test

Plot ACF and PACF

Generating the ARIMA plot

TSA Forecasting

TSA in Python

What is Model Selection?

The need for Model Selection

Cross-Validation

What is Boosting?

How Boosting Algorithms work?

Types of Boosting Algorithms

Adaptive Boosting

Cross-Validation

AdaBoost

Model Selection

Boosting algorithm using python

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.

Websoft's Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.

Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.

Websoft's Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.

It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.

It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.

After completing this Data Science Certification training, you will be able to:

Programmatically download and analyze data

Learn techniques to deal with different types of data – ordinal, categorical, encoding

Learn data visualization

Using I python notebooks, master the art of presenting step by step data analysis

Gain insight into the 'Roles' played by a Machine Learning Engineer

Describe Machine Learning

Work with real-time data

Learn tools and techniques for predictive modeling

Discuss Machine Learning algorithms and their implementation

Validate Machine Learning algorithms

Explain Time Series and its related concepts

Perform Text Mining and Sentimental analysis

Gain expertise to handle business in future, living the present

Programmatically download and analyze data

Learn techniques to deal with different types of data – ordinal, categorical, encoding

Learn data visualization

Using I python notebooks, master the art of presenting step by step data analysis

Gain insight into the 'Roles' played by a Machine Learning Engineer

Describe Machine Learning

Work with real-time data

Learn tools and techniques for predictive modeling

Discuss Machine Learning algorithms and their implementation

Validate Machine Learning algorithms

Explain Time Series and its related concepts

Perform Text Mining and Sentimental analysis

Gain expertise to handle business in future, living the present

Websoft’s Data Science certification course in Python is a good fit for the below professionals:

Programmers, Developers, Technical Leads, Architects

Developers aspiring to be a ‘Machine Learning Engineer'

Analytics Managers who are leading a team of analysts

Business Analysts who want to understand Machine Learning (ML) Techniques

Information Architects who want to gain expertise in Predictive Analytics

'Python' professionals who want to design automatic predictive models

Programmers, Developers, Technical Leads, Architects

Developers aspiring to be a ‘Machine Learning Engineer'

Analytics Managers who are leading a team of analysts

Business Analysts who want to understand Machine Learning (ML) Techniques

Information Architects who want to gain expertise in Predictive Analytics

'Python' professionals who want to design automatic predictive models