About Erudition

Erudition is a leading e-learning platform providing live instructor-led interactive online training. Wecater to professionals and students across the globe in categories like Big Data & Hadoop, Business Analytics, NoSQL Databases, Java & Mobile Technologies, System Engineering, Project Managementand Programming. We have an easy and affordable learning solution that is accessible to millions of learners. With our students spread across countries like the US, India, UK, Canada, Singapore, Australia, Middle East, Brazil and many others, we have built a community of over 1 million learners across the globe.

About Course

Erudition’s Python course helps you gain expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scikit and master the concepts like Python Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes and Q-Learning and Time Series. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR and so on.

Curriculum

Introduction to Python

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

Topics:

  • Overview of Python
  • Different Applications where Python is used
  • Values, Types, Variables
  • Conditional Statements
  • Command Line Arguments
  • The Companies using Python
  • Discuss Python Scripts on UNIX/Windows
  • Operands and Expressions
  • Loops
  • Writing to the screen

Hands On/Demo:

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

Skills:

  • Fundamentals of Python programming

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
  • Global Variables
  • Lambda Functions
  • Standard Libraries
  • The Import Statements
  • Package Installation Ways
  • Handling Multiple Exceptions
  • Function Parameters
  • Variable Scope and Returning Values
  • Object-Oriented Concepts
  • Modules Used in Python
  • Module Search Path
  • Errors and Exception Handling

Hands On/Demo:

  • Functions – Syntax, Arguments, Keyword Arguments, Return Values
  • Sorting – Sequences, Dictionaries, Limitations of Sorting
  • Packages and Module – Modules, Import Options, sys Path
  • Lambda – Features, Syntax, Options, Compared with the Functions
  • Errors and Exceptions – Types of Issues, Remediation

Skills:

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

Data Manipulation

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

Topics:

  • Basic Functionalities of a data object
  • Concatenation of data objects
  • Exploring a Dataset
  • Merging of Data objects
  • Types of Joins on data objects
  • Analysing a dataset

Hands On/Demo:

  • Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
  • Aggregation
  • Merging
  • GroupBy operations
  • Concatenation
  • 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)
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Gradient descent
  • What is Machine Learning?
  • Machine Learning Process Flow
  • Linear Regression

Hands On/Demo:

  • Linear Regression – Boston Dataset

Skills:

  • Machine Learning concepts
  • Linear Regression Implementation
  • Variables
  • Demonstrating Loops

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?
  • Algorithm for Decision Tree Induction
  • Confusion Matrix
  • What is Decision Tree?
  • Creating a Perfect Decision Tree
  • What is Random Forest?

Hands On/Demo:

  • Implementation of Logistic regression
  • Random forest
  • Decision tree

Skills:

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

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
  • PCA
  • Scaling dimensional model
  • Why Dimensionality Reduction
  • Factor Analysis
  • 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?
  • Implementing Naïve Bayes Classifier
  • Illustrate how Support Vector Machine works?
  • Grid Search vs Random Search
  • How Naïve Bayes works?
  • What is Support Vector Machine?
  • Hyperparameter Optimization
  • Implementation of Support Vector Machine for Classification

Hands On/Demo:

  • Implementation of Naïve Bayes, SVM

Skills:

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

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?
  • How does K-means algorithm work?
  • What is C-means Clustering?
  • How Hierarchical Clustering works?
  • What is K-means Clustering?
  • How to do optimal clustering
  • What is Hierarchical Clustering?

Hands On/Demo:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering

Skills:

  • Unsupervised Learning
  • Implementation of Clustering – various types

Association Rules Mining and Recommendation Systems

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

Topics:

  • What are Association Rules?
  • Calculating Association Rule Parameters
  • How does Recommendation Engines work?
  • Content-Based Filtering
  • Association Rule Parameters
  • Recommendation Engines
  • Collaborative 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
  • Elements of Reinforcement Learning
  • Epsilon Greedy Algorithm
  • Q values and V values
  • α values
  • Why Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Markov Decision Process (MDP)
  • Q – Learning

Hands On/Demo:

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

Skills:

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

Time Series Analysis

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.

Topics:

  • What is Time Series Analysis?
  • Components of TSA
  • AR model
  • ARMA model
  • Stationarity
  • Importance of TSA
  • White Noise
  • MA model
  • ARIMA model
  • ACF & PACF

Hands On/Demo:

  • Checking Stationarity
  • Implementing Dickey-Fuller Test
  • Generating the ARIMA plot
  • Converting a non-stationary data to stationary
  • Plot ACF and PACF
  • TSA Forecasting

Skills:

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

Hands On/Demo:

  • Cross-Validation
  • AdaBoost

Skills:

  • Model Selection
  • Boosting algorithm using python

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
  • Strings and related operations
  • Lists and related operations
  • Sets and related operations
  • Numbers
  • Tuples and related operations
  • Dictionaries and related operations

Hands On/Demo:

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

Skills:

  • File Operations using Python
  • Working with data types of 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
  • Indexing slicing and iterating
  • Pandas – data structures & index operations
  • matplotlib library
  • Markers, colours, fonts and styling
  • Contour plots
  • Operations on arrays
  • Reading and writing arrays on files
  • Reading and Writing data from Excel/CSV formats into Pandas
  • Grids, axes, plots
  • Types of plots – bar graphs, pie charts, histograms

Hands On/Demo:

  • NumPy library- Creating NumPy array, operations performed on NumPy array
  • Matplotlib – Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot
  • Pandas library- Creating series and dataframes, Importing and exporting data

Skills:

  • Probability Distributions in Python
  • Python for Data Visualization