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Dhaapps Datascience With Generative AI
Duration : 4 Months Live Training
+
2 Months Internship
Module 1 : Python Programming

Introduction :
➢ What is Python?
➢ Why does Data Science require
Python?
➢ Installation of Anaconda
➢ Understanding Jupyter Notebook
(IDE), Colab Notebook
➢ Basic commands in Jupyter
Notebook
➢ Understanding Python Syntax
➢ Identifiers and Operators
Data Types & Data Structures
➢ Variables, Data Types, and Strings
➢ Lists, Sets, Tuples and Dictionaries

Note : No Prerequisites

Control Flow & Conditional Statements
➢ Conditional operators, Arithmetic Operators and
Logical Operators
➢ if, else and else statements
➢ range
➢ while loops and control flow
➢ for loops and nested loops
➢ pass, break and continue
➢ Nested loops and list and dictionary comprehension
Functions and Modules
➢ What is function and types of functions
➢ Code optimization and argument functions
➢ Lambda functions
➢ map, filter and reduce
➢ Manual higher order functions & nested functions
➢ Importing a module
File handling
➢ Introduction to files
➢ Opening file
➢ File modes
➢ Reading,writing,appending data

OOPS
➢ Create A Class And Objects
➢ init (), self parameter
➢ Class Properties, Instance Properties & Static
Properties
➢ Modifying Object Properties
➢ Delete Object
➢ Pass Statements
➢ 4 pillars of oops
➢ Inheritance, Encapsulation, Polymorphism, &
Abstraction
➢ Multiple dispatch & abc modules
Exception Handling
➢ Types of Errors
➢ What is Exception?
➢ Why exception handling?
➢ Syntax error v/s Runtime error
➢ Try with multi except Handling multiple
exceptions with single except block
➢ Finally block
➢ Try-except-finally
➢ Try with finally Raise keyword
➢ Custom exceptions / User defined exceptions

Module 2 : Data Analysis using Python
Numpy - Numerical Python
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Introduction To Array
Creation & Printing Of An Array
Basic Operations In Numpy
Mathematical Functions Of Numpy
Numpy With Images
Advance Numpy Functions
Numpy Vectorization, Vectorization Vs Loops
Descriptive Stats Using Numpy

Data Manipulation with Pandas
➢ Series and DataFrames
➢ Data Importing and Exporting through Excel, CSV Files
➢ Data Understanding Operations
➢ Indexing and slicing and More filtering with Conditional Slicing
➢ Groupby, Pivot table and Cross Tab
➢ Concatenating and Merging Joining
➢ Descriptive Statistics
➢ Removing Duplicates
➢ String Manipulation

Data Visualization Using Matplotlib And Seaborn
➢ Introduction to Matplotlib
➢ Basic Plotting
➢ Properties of plotting
➢ About Subplots
➢ Line plots
➢ Pie Chart And Bar Graph
➢ Histograms
➢ Box and Violin Plots
➢ Scatterplot
➢ Joint Plot
Exploratory Data Analysis (EDA)
➢ What is EDA?
➢ Uni - Variate Analysis
➢ Bi - Variate Analysis
➢ More on Seaborn Based Plotting Including Pair
Plots, Heat Maps, Count plot along with
matplotlib plots
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What is Univariate and BI Variate Analysis?
Measures of Central Tendencies - Mean, Median, & Mode
Measures of Dispersion - Variance, Standard Deviations, Range, &
Interquartile Range
➢ Covariance and Correlation
➢ Box Plots and Outliers detection
➢ Skewness and Kurtosis
Data Gathering Techniques
➢ Data Collection Techniques
➢ Sampling Techniques:
➢ Convenience Sampling, Simple Random Sampling
➢ Stratified Sampling, Systematic Sampling and Cluster Sampling

Probability Distribution
➢ Probability And Limitations
➢ Axioms Of Probability
➢ Conditional Probability
➢ Random Variable
➢ Discrete Probability Distributions - Probability Mass Functions
➢ Bernoulli, Binomial Distribution, Poisson Distribution
➢ Continuous Probability Distributions - Probability Density Functions
➢ Normal Distribution, Standard Normal Distribution

Inferential Statistics
➢ Sampling variability and Central Limit Theorem
➢ Confidence Intervals
➢ Hypothesis Testing, A/B testing
➢ parametric vs non-parametric tests
➢ test for normality
➢ Z -test, t-test
➢ Chi – Square Test
➢ F -Test and ANOVA

Module 4 : Machine Learning
Introduction
➢ What is Machine Learning?
➢ Supervised Versus Unsupervised Learning
➢ Approaches of machine learning algorithms
➢ Decision boundaries
➢ data pre-processing
➢ tabular data pre-processing
➢ text data pre-processing
➢ image data pre-processing
➢ Under fit, optimal fit, over fit
➢ sklearn pipeline + model building
Probability Based Approach - Naive Bayes
➢ Principle of Naive Bayes Classifier
➢ Bayes Theorem
➢ Terminology in Naive Bayes
➢ Posterior probability
➢ Prior probability of class
➢ Likelihood
➢ Types of Naive Bayes Classifier
➢ Multinomial Naive Bayes
➢ Bernoulli Naive Bayes and Gaussian Naive Bayes
➢ Categorical naive bayes

Linear Algebra
➢ Introduction to Matrices
➢ Vector spaces, including dimensions, Euclidean spaces,
closure properties and axioms
➢ Eigenvalues and Eigenvectors, including how to find
Eigenvalues and the corresponding Eigenvectors
K Nearest Neighbors
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K-Nearest Neighbor Algorithm
Eager Vs Lazy learners
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Weighted knn, ball tree, kd tree, lsh forest, cosine hashing
Curse of Dimensionality
Pros and Cons of KNN
How to improve KNN performance
Hyper parameters of knn

Linear Regression
➢ Simple Linear Regression:
➢ Estimating the Coefficients
➢ Assessing the Coefficient Estimates

Multiple Linear Regression
➢ Estimating the Regression Coefficients
➢ OLS Assumptions
➢ Multicollinearity
➢ Feature Selection
➢ Gradient descent
Decision Trees
➢ Basic Terminology in Decision Tree
➢ Root Node and Terminal Node
➢ Classification Tree
➢ Regression tree
➢ Trees Versus Linear Models
➢ Advantages and Disadvantages of Trees
➢ Gini Index
➢ Overfitting and Pruning
➢ Stopping Criteria
➢ Accuracy Estimation using Decision Trees
➢ Hyper parameter tuning using random search, grid search + cross validation, kfold cv

Evaluation Metrics for Regression Techniques
➢ Homoscedasticity and Heteroscedasticity of error terms
➢ Residual Analysis
➢ Q-Q Plot
➢ Identifying the line of best fit
➢ R Squared and Adjusted R Squared
➢ M SE and RMSE
Logistic regression
➢ An Overview of Classification
➢ Difference Between Regression and classification Models
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➢ Applications and case studies across
industries
Intro To LLM
➢ History of NLP
➢ Into to large language Models
➢ What is Large Language Model
➢ Types of Large Language Model

Prompt Engineering and Working with LLM
➢ Intro To Open AI
➢ Utilizing OpenAI APIs
➢ Setting up and authenticating API usage
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➢ Understanding DALL-E and its capabilities in
image generation
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➢ Practical exercises using GPT-3/GPT4 for text generation
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Overview of the Gemini API and accessing its features
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Selecting and initializing the right model for specific tasks
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Comparison with other large language models like GPT-3 and GPT-4
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Discussion on model sizes and capabilities
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➢ Intro to the architecture of LLaMA models
➢ Understanding the differences between LLaMA model variants (8B, 13B, 30B, and
70B parameters)
➢ Implementing text generation using LLaMA

LangChain
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Introduction to the LangChain framework
Understanding the purpose and core components of LangChain Framework
LangChain Setup and necessary dependencies
Basic configuration and setup for development
Step-by-step guide to creating a simple application using LangChain
Framework
➢ Detailed walkthroughs of real-world applications built with LangChain

Bonus Courses:
SQL
Power Bi
Tableau
Linux Operating system
Networking
Cloud Computing



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