Data Science With AI Course

Module 1: PYTHON BASICS

• Introduction to Python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

Module 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

Module 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

Module 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

Module 1: OVERVIEW OF STATISTICS

• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data

Module 2: HARNESSING DATA

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data

Module 3: EXPLORATORY DATA ANALYSIS

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean, Median, And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan, And Minkowski Distance
• Covariance & Correlation

Module 4: HYPOTHESIS TESTING

• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Two Sample Relation T-test
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing

Module 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• Clustering, Classification, And Regression
• Supervised Vs Unsupervised

Module 2: PYTHON NUMPY PACKAGE

• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays

Module 3: PYTHON PANDAS PACKAGE

• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas

Module 4: VISUALIZATION WITH PYTHON – Matplotlib

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter

Module 5: PYTHON VISUALIZATION PACKAGE – SEABORN

• Seaborn: Basic Plot
• Advanced Python Data Visualizations

Module 6: ML ALGORITHM: LINEAR REGRESSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python

Module 7: ML ALGORITHM: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python

Module 8: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works: K Means theory
• Modeling in Python

Module 9: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python

Module 1: FEATURE ENGINEERING

• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering

Module 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

Module 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python

Module 4: ML ALGO: DECISION TREE

• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python

Module 5: ENSEMBLE TECHNIQUES - BAGGING

• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python

Module 6: ML ALGO: NAÏVE BAYES

• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python

Module 7: GRADIENT BOOSTING, XGBOOST

• Introduction to Boosting and XGBoost
• How does it work?
• Modeling and Evaluation in Python

Module 1: TIME SERIES FORECASTING – ARIMA

• What is Time Series?
• Trend, Seasonality, cyclical, and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python

Module 2: SENTIMENT ANALYSIS

• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews

Module 3: REGULAR EXPRESSIONS WITH PYTHON

• Regex Introduction
• Regex codes
• Text extraction with Python Regex

Module 4: ML MODEL DEPLOYMENT WITH FLASK

• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment

Module 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT…)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL

Module 6: AWS CLOUD FOR DATA SCIENCE

• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service (EC2 instance)

Module 7: AZURE FOR DATA SCIENCE

• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure

Module 8: INTRODUCTION TO DEEP LEARNING

• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python

Module 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations

Module 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MySQL Workbench installation

Module 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date, time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

Module 4: DATABASES AND TABLES (MySQL)

• Create a database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create a new table from existing data types
• Insert into, Update records
• Alter table

Module 5: SQL JOINS

• Create a database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create a new table from existing data types
• Insert into, Update records
• Alter table

Module 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

Module 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods

Module 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version Control Tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

Module 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create a New Repo with the Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub

Module 3: COMMITS, PULL, FETCH, AND PUSH

• Git Repo Introduction
• Create a New Repo with the Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub

Module 4: TAGGING, BRANCHING, AND MERGING

• Organize code with branches
• Check out the branch
• Merge branches

Module 5: UNDOING CHANGES

• Editing Commits
• Commit command, Amend flag
• Git reset and revert

Module 6: GIT WITH GITHUB AND BITBUCKET

• Creating a GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

Module 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

Module 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

Module 3: DATA TRANSFORMATION TECHNIQUES

• A TRANSFORMATION TECHNIQUE
• Exploring Query Editor
• Data Cleansing and Manipulation
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

Module 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

Module 1: ARTIFICIAL INTELLIGENCE OVERVIEW

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning

Module 2: DEEP LEARNING INTRODUCTION

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks

Module 3: TENSORFLOW FOUNDATION

• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow

Module 4: COMPUTER VISION INTRODUCTION

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network

Module 5: NATURAL LANGUAGE PROCESSING (NLP)

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On: BERT Algorithm

Module 6: AI ETHICAL ISSUES AND CONCERNS

• Issues And Concerns Around AI
• AI and Ethical Concerns
• AI and Bias
• AI: Ethics, Bias, And Trust

Module 1: NEURAL NETWORKS

• Structure of neural networks
• Neural network - core concepts (Weight initialization)
• Neural network - core concepts (Optimizer)
• Neural network - core concepts (Need of activation)
• Neural network - core concepts (MSE & RMSE)
• Feed forward algorithm
• Backpropagation

Module 2: IMPLEMENTING DEEP NEURAL NETWORKS

• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset

Module 3: DEEP COMPUTER VISION - CNN

• Convolutional neural networks (CNNs)
• CNNs with Keras
• Transfer learning in CNN
• Flowers dataset with tf2.X
• Examining x-ray with CNN model

Module 4: DEEP COMPUTER VISION - OBJECT DETECTION

• What is Object Detection
• Methods of Object Detection
• Metrics of Object Detection
• Bounding Box regression
• Labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2

Module 5: RECURRENT NEURAL NETWORK

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks
• Bi-directional RNN and LSTM
• Examples of RNN applications

Module 6: NATURAL LANGUAGE PROCESSING (NLP)

• Introduction to Natural Language Processing
• Working with Text file
• Working with a PDF file
• Introduction to regex
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects

Module 7: PROMPT ENGINEERING

• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing

Module 8: REINFORCEMENT LEARNING

• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model-free methods

Module 9: DEEP REINFORCEMENT LEARNING

• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym

Module 10: GEN AI

• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question Answer bot with the models on Hugging Face

Module 11: AUTOENCODERS

• Introduction to Autoencoders
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image