Module-2 Learning Outcomes: –  ( Job Role :- Asso Data Scientist / Machine Learning Expert ) 

Introduction to Data Science.

Different Disciplines of Data Science:

• Machine Learning
• Natural Language Processing
• Deep Learning
• Computer Vision
• Generative AI
• ChatGPT
• Data Analytics
• Business Analytics
• Applications of Machine Learning

Why Machine Learning is the Future

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

Statistics & Probability

• Descriptive Statistics and Inferential Statistics
• Sample and Population
• Variables and Data types
• Percentiles   
• Measures of Central Tendency
• Measures of Spread
• Skewness, Kurtosis
• Degrees of freedom
• Variance, Covariance, Correlation
• Descriptive statistics and Inferential Statistics in Python
• Test of Hypothesis
• Confidence Interval
• Sampling Distribution
• Standard Probability Distribution Functions
• Bernoulli, Binomial-Distributions
• Normal Distributions

Introduction to Python

• 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

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

• 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

Python in Data Manipulation

• 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
• Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
• Aggregation
• Merging
• GroupBy operations
• Concatenation
• Joining

Sequences and File Operations

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

Introduction to NumPy, Pandas and Matplotlib

• 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
• Probability Distributions in Python
• Python for Data Visualisation

Introduction to Machine Learning with Python

• 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

Supervised Learning – I & II , Dimensionality Reduction

• 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 a Random Forest?
• Implementation of Logistic Regression
• Random Forest
• Decision Tree
• Introduction to Dimensionality
• PCA
• Scaling Dimensional Model
• Why Dimensionality Reduction
• Factor Analysis
• LDA
• 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
• Implementation of Naïve Bayes, SVM

Unsupervised Learning

• What is Clustering & its Use Cases?
• How does the K-means algorithm work?
• What is C-means Clustering?
• How does Hierarchical Clustering works?
• What is K-means Clustering?
• How to do Optimal clustering
• What is Hierarchical Clustering?
• Implementing K-means Clustering
• Implementing Hierarchical Clustering

Association Rules Mining and Recommendation Systems

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

Reinforcement Learning

• 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
• Implementing Q – Learning
• Implement Reinforcement Learning using Python
• Developing Q – Learning model in Python

Time Series Analysis

• What is Time Series Analysis?
• Components of TSA
• AR Model
• ARMA Model
• Stationarity
• Importance of TSA
• White Noise
• MA Model
• ARIMA Model
• ACF & PACF
• Checking Stationarity
• Implementing the Dickey-Fuller Test
• Generating the ARIMA plot
• Converting a non-stationary data to stationary
• Plot ACF and PACF
• TSA Forecasting
• TSA in Python

Model Selection and Boosting

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

Cloud Computing & Deployment of Machine Learning Modals to Cloud

• Flask basics
• Deployment of the model on Heroku
• AWS basics 
  •  S3 
  •  EC2 
  •  AWS Lambda 
• Deployment of the model on EC2
• Deployment on AWS Lambda (Optional)
• Google Cloud Platform Basics
• Deployment of the Model on GCP
• Microsoft Azure basics
• Deployment of the Model on Azure
•  Pyspark Basics
• DeVops Concepts