Cloudruha'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. 

Introduction to Python

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

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

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

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

• Analyzing a dataset

Introduction to Machine Learning with Python

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

Supervised Learning - I

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?

Dimensionality Reduction

Topics:

• Introduction to Dimensionality

• Why Dimensionality Reduction

• PCA

• Factor Analysis

• Scaling dimensional model

• LDA

Supervised Learning - II

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

Unsupervised Learning

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?

Association Rules Mining and Recommendation Systems

Topics:

• What are Association Rules?

• Association Rule Parameters

• Calculating Association Rule Parameters

• Recommendation Engines

• How does Recommendation Engines work?

• Collaborative Filtering

• Content-Based Filtering

Reinforcement Learning

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

• α values

Time Series Analysis

Topics:

• What is Time Series Analysis?

• Importance of TSA

• Components of TSA

• White Noise

• AR model

• MA model

• ARMA model

• ARIMA model

• Stationarity

• ACF & PACF

Model Selection and Boosting

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

Sequences and File Operations

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

Introduction to NumPy, Pandas and Matplotlib

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

Project