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Data Science with Python Course (Self-Paced Learning)

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.

USD 400 USD 449

Course Overview

 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.

 

Key Highlights

  • 42 Hours of Online Self-Paced Classes
  • Real-Life Case Studies
  • Every class will be followed by practical assignments
  • Lifetime access to Learning Management System (LMS)
  • 24 x 7 Expert Support
  • Certification
  • Community forum
  • No exam included

What You'll Learn

  • 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
  •  

Career Benefits

  • Paves the road to a better career path
  • Competitive edge
  • Higher Salary
  • Growth in the organization

Who Can Attend

  • 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

Exam Formats

No Exam Included.

Course Delivery

This course is available in the following formats:

  • Self-Paced Learning Duration: 42 Hrs

Course Syllabus


Introduction to Python

Learning Objectives: You will get a brief idea of what Python is and touch on the basics. 

Topics:

  • 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

Hands On/Demo:

  • Creating “Hello World” code
  • Variables
  • Demonstrating Conditional Statements
  • Demonstrating Loops

Skills:

Fundamentals of Python programming

Sequences and File Operations

Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files. 

Topics:

  • 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

Hands On/Demo:

  • Tuple - properties, related operations, compared with a list
  • List - properties, related operations
  • Dictionary - properties, related operations
  • Set - properties, related operations

Skills:

  • File Operations using Python
  • Working with data types of Python

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

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. 

Topics:

  • 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

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

Skills:

  • Error and Exception management in Python
  • Working with functions in Python

Introduction to NumPy, Pandas and Matplotlib

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.

Topics:

  • 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


Skills:

  • Probability Distributions in Python
  • Python for Data Visualizationv

Data Manipulation

Learning Objective: Through this Module, you will understand in detail about Data Manipulation 

 

Topics:

  • 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 
  • Aggregation 
  • Concatenation 
  • Merging 
  • Joining

 

Skills:

  • Python in Data Manipulation

Introduction to Machine Learning with Python

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types. 

 

Topics:

  • 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

 

Skills:

  • Machine Learning concepts
  • Machine Learning types
  • Linear Regression Implementation

Supervised Learning - I

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc. 

 

Topics:

  • 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

 

Skills:

  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output

Dimensionality Reduction

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. 

 

Topics:

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

 

Hands-On/Demo:

  • PCA
  • Scaling

 

Skills:

  • Implementing Dimensionality Reduction Technique

Supervised Learning - II

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc. 

 

Topics:

  • 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

 

Hands-On/Demo:

  • Implementation of Naïve Bayes, SVM

 

Skills:

  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output

Unsupervised Learning

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. 

 

Topics:

  • 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?

 

Hands-On/Demo:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering

 

Skills:

  • Unsupervised Learning

Implementation of Clustering – various types

Association Rules Mining and Recommendation System

 

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm. 

 

Topics:

  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How does Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering

 

Hands-On/Demo:

  • Apriori Algorithm
  • Market Basket Analysis

 

Skills:

  • Data Mining using python
  • Recommender Systems using python

Reinforcement Learning

 

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. 

 

Topics:

  • 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
  • ? values

 

Hands-On/Demo:

  • Calculating Reward
  • Discounted Reward
  • Calculating Optimal quantities
  • Implementing Q Learning
  • Setting up an Optimal Action

 

Skills:

  • Implement Reinforcement Learning using python
  • Developing Q Learning model in python

Model Selection and Boosting

 

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. 

 

Topics:

  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

 

Hands on/Demo:

  • Cross-Validation
  • AdaBoost

 

Skills:

  • Model Selection
  • Boosting algorithm using python

 

FAQ's


What if I miss a class?

You will never miss a lecture at Upskill Yourself! You can choose either of the two options:

  • View the recorded session of the class available in your LMS
  • .You can attend the missed session, in any other live batch.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.

Who are the instructors?

All the instructors at Upskill Yourself are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts providing an awesome learning experience to the participants.

Mike Williams, Direct Consultant