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Course Curriculum

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
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
Command Line Arguments
Writing to the screen
Hands On/Demo:
Creating “Hello World” code
Demonstrating Conditional Statements
Demonstrating Loops
Fundamentals of Python programming
Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
Python files I/O Functions
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations
Hands On/Demo:
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
Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
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
Hands On/Demo:
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
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
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
Hands On/Demo:
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
Learning Objective: Through this Module, you will understand in detail about Data Manipulation
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
Hands On/Demo:
Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
GroupBy operations
Python in Data Manipulation
Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
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
Hands On/Demo:
Linear Regression – Boston Dataset
Machine Learning concepts
Machine Learning types
Linear Regression Implementation
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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?
Hands On/Demo:
Implementation of Logistic regression
Decision tree
Random forest
Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Introduction to Dimensionality
Why Dimensionality Reduction
Factor Analysis
Scaling dimensional model
Implementing Dimensionality Reduction Technique
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
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
Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
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
Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
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
Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Hands on/Demo:
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
Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
What is Model Selection?
The need for Model Selection
What is Boosting?
How Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting
Hands on/Demo:
Model Selection
Boosting algorithm using python

Course Description

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
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

The pre-requisites for edureka's Python course include the basic understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the course.

Course certification