AI , ML & Data Science course
• Python & libraries
• SQL
• Statistics
• Machine learning
• Model Optimization
• Natural language processing
• Deep learning
• Project Section
• Tableau or PowerBI
• ChatGPT & prompt engineering
• Resume building & Interview
SECTION 1: PYTHON
Module 1. Introduction
• Python - Variables and data types
• Python - Data Structures in Python ? Python - Functions and
methods
• Python - If statements
• Python - Loops
• Python - Python syntax essentials
• Python - Writing/Reading/Appending to a file ? Python - Common
pythonic errors
• Python - Getting user Input
• Python - Stats with python
• Python - Module Import
• Python – List, Multidimensional lists and Tuples ? Python - Reading
from CSV
• Python - Multi Line Print
• List Comprehension
• Python - Dictionaries
• Python - Built in functions
• Error handling
• OS module
• Python memory utilization
Module 2. Jupyter and Numpy
• Python Numpy - Introduction
• Python Numpy - Creating an Array
• Python Numpy - Reading Text Files
• Python Numpy - Array Indexing
• Python Numpy - N-Dimensional Arrays ? Python Numpy - Data
Types
• Python Numpy - Array Math
• Python Numpy - Array Methods
• Python Numpy - Array Comparison and Filtering ? Python Numpy -
Reshaping and Combining Arrays
Module 3. Pandas and Matplotlib
• Python Pandas – Introduction
• Introduction to Data Structures
• Python Pandas – Series
• Python Pandas – DataFrame
• Python Pandas – Basic Functionality ?Python Pandas – Descriptive
Statistics ?Python Pandas – Indexing and Selecting Data ?Python
Pandas – Function Application ?Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Python Pandas – Working with Text Data ?Python Pandas – Options
and Customization ?Python Pandas – Missing Data
• Python Pandas – GroupBy
• Python Pandas – Merging/Joining
• Python Pandas – Concatenation
• Python Pandas – IO Tools
• Python Pandas – Dates Conversion
• One industry case study analysis as EDA (exploratory data
analytics) in pandas
SECTION 2: SQL
Module 4. SQL for Data Science
• Install SQL packages and Connecting to DB
• Basics of SQL DB, Primary key, Foreign Key
• SELECT SQL command, WHERE Condition
• Retrieving Data with SELECT SQL command and WHERE Condition to
Pandas Data frame
• SQL Functions (Max, Min, Count …)
• SQL Wildcards
• SQL JOINs
• Left Join, Right Joins, Multiple Joins
• SQL Select and Insert Functions
• SQL Stored Procedures
• SQL Create and Drop Database
• SQL Create, Update, Alter, Delete and Drop Table
• SQL Constraints
SECTION 3: STATISTICS
Module 5. Statistics
• Inferential Statistics
o Basics of Probability
o Discrete and Continuous Probability Distributions
o Central Limit Theorem
• Hypothesis Testing
• Exploratory Data Analysis
o Data Sourcing
o Data Cleaning
o Univariate and Bivariate Analysis
o Derived Metrics
SECTION 4: Machine Learning Algorithms
Module 6. Machine Learning - Introduction
• What is Machine Learning
• Types of Machine Learning
• Applications of Machine Learning
• Supervised vs Unsupervised learning
• Classification vs Regression
• Training and testing Data
• features and labels
Module 7. Linear Regression
• Introduction
• Introducing the form of simple linear regression
• Estimating linear model coefficients
• Interpreting model coefficients
• Using the model for prediction
• Plotting the "least squares" line
• Quantifying confidence in the model
• Identifying "significant" coefficients using hypothesis testing and p
values
• Assessing how well the model fits the observed data
• Extending simple linear regression to include multiple predictors
? Comparing feature selection techniques: R-squared, p-values, cross validation
• Creating "dummy variables" (using pandas) to handle categorical predictors
Module 8. Logistic Regression
• Refresh your memory on how to do linear regression in scikit-learn
? Attempt to use linear regression for classification
• Show you why logistic regression is a better alternative for classification
• Brief overview of probability, odds, e, log, and log-odds
? Explain the form of logistic regression
• Explain how to interpret logistic regression coefficients
?
Demonstrate how logistic regression works with categorical
features
• Compare logistic regression with other models
Module 9. Support Vector Machine
• Introduction
• Tuning parameters
• Kernel
• Regularization
• Gamma
• Margin
• Classification Example
Module 10. Naive Bayes
• Introduction
• Working Example
Module 11. K-Means Clustering
• Introduction
• Unsupervised Learning
• K-Means Algorithm
• Optimization Objective
• Random Initialization
• Choosing the number of clusters
Module 12. KNN
• Introduction
• Working Example
Module 13. Decision Trees and Random Forests
• Introduction to Decision Trees
• Truncation and Pruning
• Random Forests
Module 14. Natural Language Processing
• Introduction to NLTK
• Stop words
• Stemming
• Lemmatization
• Named entity recognition
• Text classification
• Sentiment analysis
•
SECTION 5: MODEL OPTIMIZATION
Module 15. Model Optimization and Evaluation
• Maxima and Minima
• Gradient Descent
• Stochastic Gradient Descent
SECTION 6: Natural Language processing
Module 16. NLP
• Introduction
• Feature extraction
• Syntactic & semantic analysis
• Use cases
SECTION 7: DEEP LEARNING
Module 17. Artificial Neural Network
• Introduction
• Cost Function
• Backpropagation Algorithm
• Working Example
• Convolutional Neural Networks(CNNs)
• Recurrent Neural Networks(RNNs)
Module 18. Tenserflow
SECTION 8: Project Section
( Students needs to spend time to complete project )
Module 19. Project Section
• Python Project -Introduction
• Python Project -Housing Data Set or specific Data Set from Kaggle
• Python Project -Understand the problem ? Python Project
-Hypothesis Generation ? Python Project -Get Data
• Python Project -Data Exploration
• Python Project -Data Pre-Processing ? Python Project -Feature
Engineering ? Python Project -Model Training
• Python Project -Model Evaluation
SECTION 9. Data Visualization ( PowerBI/ Tableau).
• Significance of different Data visualization tool in industry for telling stories with DATA
• PowerBI data model creation for analysis, Data connection , data points
• One complete case study with PowerBI data analysis
• Tableau advantage of data visualization
• Denodo introduction and future role
SECTION 10. Chat GPT, LLM, prompt engineering,
• Engineering ? Python Project -Model Training
• Introduction
• LLM
• Chat GPT Architecture
• Prompt engineering
• Creating working prototype
SECTION 11. Resume building & mock interview
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