**PYTHON INTRODUCTION**

Lecture:1 What is Python?

Lecture:2 Python History

Lecture:3 Python 2.x vs 3.x

Lecture:4 Features of python

Lecture:5 About Python Versions

Lecture:6 Applications of python

**INSTALLATION OF PYTHON**

Lecture:7 How to install Python

Lecture:8 Python Script mode

Lecture:9 Python GUI mode

Lecture:10 Python Interactive Mode

Lecture:11 Python In Linux

Lecture:12 How to execute a python script

Lecture:13 Windows GUI mode

Lecture:14 How to Install ANACONDA

Lecture:15 How to set Path

**BASICS OF PYTHON**

Lecture:16 Python “Hello World”

Lecture:17 How to Execute Python

Lecture:18 Variables in python

Lecture:19 Keywords in python

Lecture:20 Identifiers in python

Lecture:21 Literals in python

Lecture:22 Operators in python

Lecture:23 Comments in python

**PYTHON STRINGS**

Lecture:24 Accessing Strings

Lecture:25 Strings Operators

Lecture:26 Basic Operators

Lecture:27 Membership Operators

Lecture:28 Relational Operators

Lecture:29 Slice Notation

Lecture:30 String Functions and Methods

**PYTHON LISTS**

Lecture:31 How to define list

Lecture:32 Accessing list

Lecture:33 Elements in a Lists

Lecture:34 List Operations

Lecture:35 Adding Lists

Lecture:36 Replicating lists

Lecture:37 List slicing

Lecture:38 Updating elements in a List

Lecture:39 Appending elements to a List

Lecture:40 Deleting Elements from a List

Lecture:41 Functions and Methods of Lists

**PYTHON TUPLES**

Lecture:42 How to define tuple

Lecture:43 Accessing tuple

Lecture:44 Elements in a tuple

Lecture:45 Tuple Operations

Lecture:46 Tuple slicing

Lecture:47 Deleting tuple

Lecture:48 Functions and Methods of tuple

**PYTHON DICTIONARY**

Lecture:49 How to define dictionary

Lecture:50 Accessing Dictionary

Lecture:51 Updating

Lecture:52 Deletion

Lecture:53 Functions and Methods

**PYTHON CONTROL STATEMENTS**

Lecture:54 “If” in python

Lecture:55 “If else” in python

Lecture:56 “else if” in python

Lecture:57 “nested if” in python

Lecture:58 “for loop” in python

Lecture:59 “while loop” in python

Lecture:60 “break” in python

Lecture:61 “continue” in python

Lecture:62 “pass” in python

**PYTHON FUNCTIONS**

Lecture:63 Defining a Function

Lecture:64 Invoking a Function

Lecture:65 return Statement

Lecture:66 Argument and Parameter

Lecture:67 Passing Parameters

Lecture:68 Default Arguments

Lecture:69 Keyword Arguments

Lecture:70 Anonymous Function

Lecture:71 Difference between Normal Functions and Anonymous Function

Lecture:72 Scope of Variable

**PYTHON I/O**

Lecture:73 “print” statement

Lecture:74 Input from Keyboard

**FILE HANDLING**

Lecture:75 Operations on Files

Lecture:76 Opening file

Lecture:77 closing file

Lecture:78 reading file

Lecture:79 writing file

Lecture:80 Modes of files

Lecture:81 Methods in files

**PYTHON OOPS CONCEPT**

Lecture:82 Python OOPs Concepts

Lecture:83 Python Object Class

Lecture:84 Python Constructors

Lecture:85 Python Inheritance

Lecture:86 Multilevel Inheritance

Lecture:87 Multiple Inheritance

Lecture:88 Operator Overloading

Lecture:89 Function Overriding

**PYTHON MODULES**

Lecture:88 Importing a Module

Lecture:89 Example of importing multiple modules

Lecture:90 How to use “from” import statement

Lecture:91 import whole module

Lecture:92 Built in Modules in Python

Lecture:93 Package

**PYTHON EXCEPTIONS**

Lecture:94 What is Exception handling

Lecture:95 Declaring Multiple Exception

Lecture:96 Finally Block

Lecture:97 Raise an Exception

Lecture:98 Custom Exception

**PYTHON DATE AND TIME**

Lecture:99 Retrieve Time

Lecture:100 Formatted Time

Lecture:101 time module

Lecture:102 date module

Lecture:103 date time module

**ADVANCED CONCEPTS OF PYTHON**

Lecture:104 Math module

Lecture:105 Random module

Lecture:106 Sys module

Lecture:107 List Comprehension

Lecture:108 Closure

Lecture:109 Iterator

Lecture:110 Generator

Lecture:111 Decorators

Lecture:112 Map function

Lecture:113 Filtering

Lecture:114 Zipping

Lecture:115 with Statement

Lecture:116 Keyword arguments

Lecture:117 *args vs. ** kwargs

Lecture:118 Code Introspection

Lecture:119 Multithreading

Lecture:120 Regular Expressions

Lecture:121 Web Scraping using BeautifulSoup

Lecture:122 Extracting data from Excel File

Lecture:123 Extracting data from MS-Word file

Lecture:124 Extracting data from PDF

**INTERVIEW TIPS**

Lecture:104 Interview Oriented Questions and Answers Discussion.

**DATA SCIENCE INTRO**

Introduction to data science

Stages of data science

Prerequisites to become a Data Scientist

Job scope in data science

Tools and packages used for data analytics

Environmental Setup

**NUMPY PACKAGE**

Introduction to numpy

Introduction to data analysis

Numpy environmental setup

Datatypes

Ndarray Object

Array attribute

Array Creation

Indexing and Slicing

Array from Existing Data

Advanced Indexing

Array from Numerical ranges

Array Iteration

Array Manipulation

Broadcasting

Binary Operators

String Functions

Mathematical functions

Arithmetic Operations

Sort, Search, Counting functions

Statistical Functions

Byte swapping

Copies and Views

Matrix Library

Linear Algebra

Matplotlib

Histogram using Matplotlib

I/O with numpy

**DATA MANIPULATION AND ANALYSIS USING PANDAS**

Introduction to pandas

Pandas environmental setup

Introduction to Data Structure

Pandas Series

Basic Functionality

Dataframe, Panel

Function Application

Descriptive Statistics

Reindexing

Iteration

Sorting

Working with Text data

Selecting Data

Statistical Functions

Aggregation

Missing data

Group by

Merging or joining

Concatenation

Date functionality

Time delta

Categorical data

Data Visualization

I/O Tools

Sparse Data

Comparison with SQL

**DATA VISUALISATION USING MATPLOTLIB AND SEABORN**

Introduction to data visualisation

Chart properties

Chart styling

Heat Maps

Box plots

Scatter plots

Bubble charts

3D charts

Time Series

Graph data

Geographical data

Importing datasets and libraries

Color Palette

Histogram

Visualize pairwise relationship

Statistical Estimation

Kernel Density Estimation

Facet Grid

Pair Grid

Visualizing Statistical Relationships

Introduction to SQL

Database Normalization and Entity Relationship Model(self-paced)

SQL Operators

Working with SQL: Join, Tables, and Variables

Deep Dive into SQL Functions

Working with Window functions

Working with Subqueries

SQL Views, Functions, and Stored Procedures

Deep Dive into User-defined Functions

SQL Optimization and Performance

Importing and Exporting Databases

Advanced Topics

Managing Database Concurrency

Practice Session

Case Study

Writing comparison data between the past year and to present year concerning top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Introduction to R

R Packages

Sorting DataFrame

Matrices and Vectors

Reading Data from External Files

Generating Plots

Analysis of Variance (ANOVA)

Association Rule Mining

Regression in R

Analyzing Relationship with Regression

Advanced Regression

Logistic Regression

Advanced Logistic Regression

Database Connectivity with R

Integrating R with Hadoop

R Case Studies

R Packages

Sorting DataFrame

Matrices and Vectors

Reading Data from External Files

Generating Plots

Analysis of Variance (ANOVA)

Association Rule Mining

Regression in R

Analyzing Relationship with Regression

Advanced Regression

Logistic Regression

Advanced Logistic Regression

Database Connectivity with R

Integrating R with Hadoop

R Case Studies

The measure of central tendency, the measure of spread, five points summary, etc.

Probability

Probability Distributions, Probability in Data Science

Probability Distributions, Binomial distribution, Poisson distribution, Bayes’ Theorem, central limit theorem
Inferential Statistics

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test,A/B Testing

Hypothesis Testing

Type-I error

Type-II error

Case Study
This case study will cover the following concepts:

Building a statistical analysis model that uses quantification, representations, and experimental data

Reviewing, analyzing, and drawing conclusions from the data

Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.

SaaS Connectors, Working with Azure SQL database, Python, and R with Power BI

Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.

DAX

Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

Slicers, filters, Drill Down Reports

Power BI Query, Q & A and Data Insights

Row Level Security(RLS), Dynamic Title Reports

Power BI Settings, Administration and Direct Connectivity

Case Study:

This case study will cover the following concepts:

Creating a dashboard to depict actionable insights in sales data

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.
Data Visualization with Excel

Charts, Pie charts, Scatter, and bubble charts

Bar charts, Column charts, Line charts, Maps

Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Ensuring Data and File Security

Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing an action with Sub and ending Sub if condition not met.
Statistics with Excel

ONE-TAILED TEST AND TWO-TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL

**Introduction to Machine Learning**

1.Supervised, Unsupervised learning.

2.Introduction to scikit-learn, Keras, etc.
Regression

3.Introduction classification problems, Identification of a regression problem, dependent and independent variables

4.How to train the model in a regression problem?

5.How to evaluate the model for a regression problem?

6.How to optimize the efficiency of the regression model?
**Classification**

1.Introduction to classification problems, Identification of a classification problem, dependent and independent variables

2.How to train the model in a classification problem?

3.How to evaluate the model for a classification problem?

4.How to optimize the efficiency of the classification model?
**Clustering**

1.Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables

2.How to train the model in a clustering problem?

3.How to evaluate the model for a clustering problem?

4.How to optimize the efficiency of the clustering model?
**Supervised Learning**

1.Linear Regression-Creating linear regression models for linear data using statistical tests.

2.Logistic Regression-Creating logistic regression models for classification problems.

3.Support Vector Machine(SVM)

4.Naive Bayes Classifier

5.K-Nearest Neighbour(KNN)

6.Decision Tree

7.Random Forest Classifier
**Ensemble Learning**

1.Introduction to Baggging and Boosting Algorithms

2.AdaBoost (Adaptive Boosting) Algorithm

3.Gradient Boosting Machines (GBM) Algorithm

4.XGBoost (Extreme Gradient Boosting) Algorithm

5.LightGBM (Light Gradient Boosting Machine) Algorithm

6.Stochastic Gradient Boosting Algorithm

7.LPBoost (Linear Programming Boosting) Algorithm
**Unsupervised Learning**

1.K-means-The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach

2.Dimensionality reduction-Handling multi dimensional data and standardizing the features for easier computation

3.Linear Discriminant Analysis-LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data

4.Principal Component Analysis-PCA follows the same approach in handling multidimensional data
**Performance Metrics**

1.Classification reports-To evaluate the model on various metrics like recall, precision, f-support, etc.

2.Confusion matrix-To evaluate the true positive/negative, and false positive/negative outcomes in the model.
r2, adjusted r2, mean squared error, etc.

3.Time Series Forecasting-Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting
**Time Series Forecasting **

1.Resampling, Autocorrelation, Forecasting, Seasonal

2.Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests

3.Autoregressive methods, moving averages, ARIMA, SARIMA

**Data Visualization Using Tableau**

Introduction to Data Visualization

Introduction to Tableau

Basic Charts and Dashboard

Descriptive Statistics, Dimensions and Measures

Visual Analytics: Storytelling through Data

Dashboard Design & Principles

Advanced Design Components/ Principles: Enhancing the Power of Dashboards

Special Chart Types

Tableau to Analyze Data

Connect Tableau to a variety of dataset

Analyze, Blend, Join and Calculate Data

Tableau to Visualize Data

Visualize Data In the form of Various Charts, Plots and Maps

Data Hierarchies

Work with Data Blending in Tableau

Work with Parameters

Create Calculated Fields

Adding Filters and Quick Filters

Create Interactive Dashboards

Adding Actions to Dashboard

Case Study: Hands-on Using Tableau

Integrate Tableau with Google Sheets

**[ARTIFICIAL INTELLIGENCE (AI)]**

What is AI?

Types of AI.

Advantages of AI.

Applications of AI in Current Era.

What is Reinforcement Learning?

Types of RL.

Advantages of Reinforcement Learning.

Applications of Reinforcement Learning.

Models used for Reinforcement Learning.

Key features of RL.

Elements of RL.

What is Agent?

What is Environment?

What is reward and Punishment in RL.

Bellman Equation.

Reinforcement Learning Models.

Markov Decision Process.

What is Q-Learning?

Application of RL Models.
**[Natural Language Processing (NLP)]**

What is NLP?

Advantages of NLP.

Applications of NLP in Practical Fields.

Types of NLP.

Various Tools/Libraries used for NLP.

Installing NLP Packages.

Working with NLTK Package.

Rule based NLP vs. Statistical NLP.

Tokenizing words and sentences with nltk.

Word Level Analysis.

Syntactic Analysis.

Semantic Analysis.

Part of Speech Tagging(PoS)

Stemming, Lemmatization

Chunking, Chinking

Named Entity Recognition (NER).

WordNet

Bag of Words.

Practical Project Implementation of NLP.
** [COMPUTER VISION]**

What is Computer Vision?

Need of Computer Vision?

Packages used for Computer Vision.

Introduction to Computer Vision

Introduction to OpenCV

Installing OpenCV

Storing Images

Reading Images

Writing Images

Image Conversion

Colored Image to Binary

Grayscale to Binary

Drawing Functions

Drawing a Circle

Drawing a Line

Drawing a Rectangle

Drawing an Ellipse

Drawing Polylines

Adding Text

Blur Techniques

Filtering

Bilateral Filter

Box Filter

SQRBox Filter

Filter2D

Dilation

Erosion

Morphological Operations

Image Pyramids

Thresholding

Simple Threshold

Adaptive Threshold

Adding Borders

Transformation Operations

Laplacian Transformation

Distance Transformation

Camera and Face Detection

Face Detection in a Picture

Face Detection using Live Camera

Geometric Transformation

Affine Translation

Rotation

Scaling

Color Maps

Canny edge Detection

Practical Project Implementation using Open CV.
** [DEEP LEARNING]**

Introduction to Deep Learning.

Advantages of Deep Learning.

Applications of Deep Learning.

Packages used for Deep Learning.

Models used for Deep Learning.

What is Neural Networks?

Types of Artificial Neural Networks.

Convolutional Neural Networks(CNN)

Application of CNN.

Intuition behind CNN.

Practical Implementation of CNN.

Recurrent Neural Network (RNN).

Application of RNN.

Intuition behind RNN.

Practical Implementation of RNN.

LSTM (Long Short Term Memory).

Application of LSTM.

Multilayer Perceptron.

Application of Multilayer Perceptron.

Generative Adversarial Networks (GANs).

Restricted Boltzman Machine (RBM).

Deep Belief Network.

Auto Encoder.

Application of Deep Learning Models.
** [DEEP LEARNING WITH KERAS]**

Introduction to Keras.

Installing Keras.

Keras Layers.

Keras Models

Keras Model Compilation

Regression Prediction using MPL.

Time Series Prediction using LSTM and RNN.

Keras Vs. Tensorflow vs.PyTorch Vs.CNTK Vs.Theano

Project Implementations using Keras.
** [DEEP LEARNING WITH TENSORFLOW]**

Introduction to Tensorflow.

Installing Tensorflow.

CPU vs. GPU vs. TPU.

Tensorflow Basics: Tensors, Shapes, Types, Sessions, Operators.

What is Tensor board?

What is Placeholder?

Data frame and Data range use of pandas.

How to import CSV Datasets in TF?

Linear regression using TF.

Tensorflow Optimizers.

Gradient Decent Optimization.

Convolutional Neural Network using TF.

Recurrent Neural Networks using TF.

CNN vs. RNN

Multilayer Perceptron using TF.

Hidden Layer Perceptron.

Practical Project Implementations of Tensorflow.

What is Google Colab?

How to run Tensor Flow on Google Colab.

What is SAS?

Why SAS is popular in job market?

SAS Modules

How to download and install SAS Software

Base SAS Tutorials

SAS Tutorial for Beginners

How to Import Data

How to Export Data

Data Manipulation and Analysis with SAS

SAS Functions

Advanced SAS : Proc SQL

Advanced SAS : SAS Macros

Practical Problem-Solving SAS Examples

SAS Analytics / Statistics Tutorial

SAS Certification Questions and Answers

SAS Interview Questions and Answers

Analytics Companies using SAS

Data Visualizations with SAS Graphs

The SPSS Environment

The Data View Window

Using SPSS Syntax

Data Creation in SPSS

Importing Data into SPSS

Variable Types

Date-Time Variables in SPSS

Defining Variables

Creating a Codebook

Working with DataToggle Dropdown

Computing Variables

Recoding Variables

Recoding String Variables (Automatic Recode)

Weighting Cases

Rank Cases

Sorting Data

Grouping Data

Data Mining - Intro

Data Mining - Tools

Data Mining - Issues

Data Mining - Evaluation

Data Mining - Terminologies

Data Mining - Knowledge Discovery

Data Mining - Systems

Data Mining - Query Language

Classification & Prediction

Data Mining - Decision Tree Induction

Data Mining - Bayesian Classification

Rules Based Classification

Data Mining - Classification Methods

Data Mining - Cluster Analysis

Data Mining - Mining Text Data

Data Mining - Mining WWW

Data Mining - Applications & Trends
**Data Mining with RapidMiner**

What is RapidMiner ?

Rapidminer as a Data Mining Interpreter

Process setup records

As a matter of fact you have two separate cycles

WindowExamples2OriginalDat

RapidMiner Products

RapidMiner Auto Model

RapidMiner Turbo Prep

RapidMiner Go

RapidMiner Server

RapidMiner Radoop

**Web Scraping**

What is Web Scraping?

Scrape and Parse Text From Websites

Build Your First Web Scraper

Extract Text From HTML With String Methods

Get to Know Regular Expressions

Extract Text From HTML With Regular Expressions

Python Modules for Web Scraping

Legality of Web Scraping

Data Extraction

Data Processing

Processing Images and Videos

Dealing with Text

Scraping Dynamic Websites

Scraping Form based Websites

Processing CAPTCHA

Use an HTML Parser for Web Scraping in Python

Install Beautiful Soup

Create a BeautifulSoup Object

Use a BeautifulSoup Object

Check Your Understanding

Interact With HTML Forms

Install MechanicalSoup

Create a Browser Object

Submit a Form With MechanicalSoup

Interact With Websites in Real Time

**Introduction to Big Data Hadoop**

What is Big Data?

What is Hadoop?

Hadoop Installation

**Hadoop Modules**

HDFS

Yarn

MapReduce

HBase

**HBase**

What is HBase?

HBase Model

HBase Read

HBase Write

HBase MemStore

HBase Installation

RDBMS vs HBase

HBase Commands

HBase Example

**Hive**

What is Hive?

Hive Installation

Hive Data Types

Hive Partitioning

Hive Commands

Hive DDL Commands

Hive DML Commands

Hive Sort by Order by

Hive Joins

**Pig**

What is Pig?

Pig Installation

Pig Run Modes

Pig Latin Concepts

Pig Data Types

Pig Example

Pig UDF

**Sqoop**

What is Sqoop?

Sqoop Installation

Starting Sqoop

Sqoop Import

Sqoop Where

Sqoop Export

**Spark Intro**

Spark Installation

Spark Architecture

Spark Components

**Spark RDD**

What is RDD

RDD Operations

RDD Persistence

RDD Shared Variables

**In-built Functions**

Map FunctionFiler

FunctionCount

FunctionDistinct

FunctionUnion

FunctionIntersection

FunctionCartesian

FunctionsortByKey

FunctiongroupByKey

FunctionreducedByKey

FunctionCo-Group

FunctionFirst

FunctionTake Function

**PySpark Basics**

PySpark – Features

PySpark – Advantages

PySpark – Modules & Packages

PySpark – Cluster Managers

PySpark – Install on Windows

PySpark – Install on Mac

PySpark – Web/Application UI

PySpark – SparkSession

PySpark – SparkContext

PySpark – RDD

PySpark – Parallelize

PySpark – repartition() vs coalesce()

PySpark – Broadcast Variables

PySpark – Accumulator

**PySpark DataFrame**

PySpark – Create an empty DataFrame

PySpark – Convert RDD to DataFrame

PySpark – Convert DataFrame to Pandas

PySpark – show()

PySpark – StructType & StructField

PySpark – Column Class

PySpark – select()

PySpark – collect()

PySpark – withColumn()

PySpark – withColumnRenamed()

PySpark – where() & filter()

PySpark – drop() & dropDuplicates()

PySpark – orderBy() and sort()

PySpark – groupBy()

PySpark – join()

PySpark – union() & unionAll()

PySpark – unionByName()

PySpark – UDF (User Defined Function)

PySpark – transform()

PySpark – apply()

PySpark – map()

PySpark – flatMap()

PySpark – foreach()

PySpark – sample() vs sampleBy()

PySpark – fillna() & fill()

PySpark – pivot() (Row to Column)

PySpark – partitionBy()

PySpark – MapType (Map/Dict)

**PySpark SQL Functions**

PySpark – Aggregate Functions

PySpark – Window Functions

PySpark – Date and Timestamp Functions

PySpark – JSON Functions

**PySpark Datasources**

PySpark – Read & Write CSV File

PySpark – Read & Write Parquet File

PySpark – Read & Write JSON file

PySpark – Read Hive Table

PySpark – Save to Hive Table

PySpark – Read JDBC in Parallel

PySpark – Query Database Table

PySpark – Read and Write SQL Server

PySpark – Read and Write MySQL

PySpark – Read JDBC Table

**Snowflake**

Snowflake - Introduction

Snowflake - Data Architecture

Snowflake - Functional Architecture

Snowflake - How to Access

Snowflake - Editions

Snowflake - Pricing Model

Snowflake - Objects

Snowflake - Table and View Types

Snowflake - Login

Snowflake - Warehouse

Snowflake - Database

Snowflake - Schema

Snowflake - Table & Columns

Snowflake - Load Data From Files

Snowflake - Sample Useful Queries

Snowflake - Monitor Usage and Storage

Snowflake - Cache

Unload Data from Snowflake to Local

Snowflake – Create Database

SnowSQL – CREATE TABLE LIKE

SnowSQL – CREATE TABLE as SELECT

SnowSQL – Load CSV file into Table

SnowSQL – Load Parquet file into table

SnowSQL – Unload Snowflake Table to CSV file

SnowSQL – Unload Snowflake table to Parquet file

SnowSQL – Unload Snowflake table to Amazon S3

Snowflake – Spark Connector

Snowflake – Spark DataFrame write into Table

Snowflake – Spark DataFrame from table

**Introduction to Amazon SageMaker**

Get started on SageMaker

Customer Churn Prediction with XGBoost

**Prepare data**

Amazon SageMaker Data Wrangler

Distributed Data Processing using Apache Spark and SageMaker Processing

Get started with SageMaker Processing

**Train and tune models**

Hyperparameter Tuning with the SageMaker TensorFlow Container

**Deploy models**

Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo

Use SageMaker Batch Transform for PyTorch Batch Inference

**Track, monitor, and explain models**

Amazon SageMaker Model Monitor

**Orchestrate workflows**

Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines

SageMaker Pipelines Lambda Step

**Popular frameworks**

Hugging Face Sentiment Classification

Iris Training and Prediction with Sagemaker Scikit-learn

Train an MNIST model with TensorFlow

Train an MNIST model with PyTorch

**BigML**

BigML - Using a Decision Tree Model

BigML - Using an Ensemble

BigML - Using a Deepnet Model

BigML - Using a Linear Regression

BigML - Using a Logistic Regression

BigML - Using a Fusion Model

BigML - Using a Time Series

BigML - Using an OptiML

BigML - Using a Cluster

BigML - Using an anomaly detector

BigML - Using a Topic Model

BigML - Using Association Discovery

BigML - Using a PCA

BigML - Creating and executing scripts

BigML - Images Classification

BigML - Images Feature Extraction

BigML - Images Object Detection

**MLOps Introduction**

What is MLOps?

MLOps Lifecycle

MLOps Capabilities

**Getting Started with MLOps**

MLOps - ML Development

MLOps - Model Building and Training

MLOps - Training Operationalisation

MLOps - Model Versioning

MLOps - Model Registry

MLOps - Model Governance

MLOps - Model Deployment

MLOps - Prediction Serving

MLOps - Model Monitoring

**MLflow**

What is MLflow?

Install MLflow, instrument code & view results in minutes

Compare runs, choose a model, and deploy it to a REST API

MLflow Tracking

MLflow LLM Tracking

MLflow Projects

MLflow Models

MLflow Model Registry

MLflow Recipes

MLflow AI Gateway (Experimental)

MLflow Plugins

MLflow Authentication

MLflow vs. Airflow

**StreamLit**

What is Streamlit?

Why should data scientists use Streamlit?

How to use Streamlit

Install Streamlit

How to run your Streamlit code

Display texts with Streamlit

Display an image, video or audio file with Streamlit

Input widgets

Display progress and status with Streamlit

Sidebar and container

Display graphs with Streamlit

Display maps with Streamlit

Themes

Build a machine learning application

How to deploy a Streamlit app

**Generative AI**

1. Foundations of Generative AI

a. Machine Learning (ML) Paradigms

b. Neural Networks, Architectures, Activation Functions, Optimization Techniques

c. Representation Learning, Embeddings, Feature Engineering

d. Probabilistic Models, Bayesian Networks, Hidden Markov Models (HMMs)

e. Reasoning and Planning

f. Natural Language Processing, Tokenization, Part-of-Speech (POS) tagging,
Named Entity Recognition (NER), Word2Vec

g. Computer Vision, Image classification, Object detection, Image segmentation

h. Foundation Models and Their Roles

2. Language Modeling and Transformers

a. Sequential Data Modeling, Recurrent Neural Networks (RNNs),
Encoder-Decoder Models

b. Natural Language Generation and Understanding

c. Multilingual Language Models, Cross-lingual learning

d. Attention Mechanisms, Transformers, Self-attention, Multi-head attention

e. Pre-trained Transformers: BERT, GPT, T5, XLNet, LaMDA, etc.

f. Conditional Language Generation, Text summarization, Question answering

g. Textual Encoding and Decoding: Tokenization, Byte Pair Encoding (BPE)

3. Large Language Models

a. Weight, Bias and Parameters of Language Models

b. Reasoning and Commonsense Knowledge Integration

c. Multimodal Learning and Embeddings

d. Memory and Efficiency Optimization

e. GPT, LLaMA, LaMDA, PaLM, Gemini, Falcon, BLOOM

f. Zero-shot and Few-shot Learning

g. Evaluation Metrics for LLMs

4. Generative AI and LLM Frameworks

a. Tensorflow and PyTorch

b. Hugging Face

c. Lang Chain

d. Llama Index

e. Generative AI providers - OpenAI, Cohere, Anthropic, LLMFlow

f. Generative AI Agents, AutoGPT, AgentGPT, BabyAGI

g. Code Generative Tools - Amazon CodeWhisper, OpenAI Codex

h. Open-source Tools and Resources for Generative AI

5. Image Generative Models

a. Autoencoder and its Variants

b. Generative Adversarial Networks (GANs)

c. Style Transfer and Image Transformation

d. Latent Diffusion Model

e. Stable Diffusion

f. DALL.E

g. Contrastive Language-Image Pre-Training (CLIP)

h. Attention Mechanisms in Image Generation

i. Hierarchical Text-Conditional Image Generation with CLIP Latents

6. Prompt Engineering

a. Prompt Design Strategies

b. Task Formulation in Prompts

c. Prompt Patterns

d. Prompt Tuning Techniques

e. Fine-tuning Prompts for Specific Tasks

f. Domain-specific Prompt Engineering

g. Dynamic and Adaptive Prompting

h. Zero-shot learning, Chain-of-thought, Self-consistency

i. Evaluating Prompt Performance

7. Vector Databases and Search

a. Vector Databases

b. High-dimensional Data Storage

c. Vector Embeddings

d. High-Dimensional Semantic Similarity

e. Personalized Search, Multimodal Search, Knowledge Graph Search

f. Semantic search, Conversational search, Visual search

g. Personalization and Relevance Ranking

h. Evaluation of Search Systems

8. Fine Tuning and Optimizing the LLMs

a. Hyperparameter Tuning and Optimization

b. Data Augmentation for Fine-tuning

c. Prompt Tuning

d. Retrieval-Augmented Generation (RAG)

e. Parameter Efficient Fine Tuning (PEFT) Techniques

f. Reinforcement Learning from Human Feedback (RLHF)

g. Efficient Training Pipeline

9. Deployment and Scaling of Generative Models

a. Model Deployment Strategies

b. Scalability and Resource Management for Generative Models

c. Automated Pipelines for Model Deployment

d. Versioning and Rollback Strategies

e. Model Monitoring and Performance Tracking

f. Interoperability and Compatibility

g. Robustness and Error Handling in Model Deployment

h. Security Measures in Model Deployment

i. AIOps and LLMOps

Graph Theory Fundamentals

Graph Representation and Storage

Graph Databases and Query Languages

Advanced Graph Algorithms

Graph-Based Feature Engineering

Graph Machine Learning

Graph Neural Networks (GNNs)

**Introduction to Quantum Computing**

Basics of Quantum Mechanics and Quantum Gates

Quantum Algorithms (Shor's Algorithm, Grover's Search)

Quantum Information Theory and Quantum Entanglement

Quantum Circuit Design and Simulation

**Quantum Machine Learning (QML)**

Variational Quantum Classifiers and Quantum SVM

Quantum Neural Networks (QNNs)

Hybrid Quantum-Classical Algorithms

Use Cases in Optimization, Cryptography, and Drug Discovery

**Blockchain Fundamentals for Data Scientists**

Blockchain-based Data Storage and Security

Smart Contracts and Data Integrity

Hands-On: Ethereum, Hyperledger, Solidity

**Decentralized Machine Learning**

Federated Learning on Blockchain

Data Sharing in Decentralized Networks

Privacy and Security in Decentralized Data Models

Real-World Applications: Energy Trading, Supply Chain, AI Governance

1.Building chatbots

2.Credit card fraud detection

3.Fake news detection

4.Forest fire prediction

5.Classifying breast cancer

6.Driver drowsiness detection

7.Recommender systems

8.Sentiment analysis

9.Exploratory data analysis

10.Gender detection and age detection

11.Recognizing speech emotion

12.Customer segmentation

13.Analyzing the impact of climate change on global food supply

14.Weather Prediction

15.Keyword generation for google ads

16.Traffic Signs Recognition

17.Wine Quality Analysis

18.Stock Market Prediction

19.Video Classification

20.Human Action Recognition

21.Medical Report Generation using CT Scans

21.Email Classification

22.Uber Data Analysis

23.Sound Classification

24.Credit Card Fraud Detection

25.Sign Language Recognition

26.Class of Flower Prediction

27.Colour Detection

28.Loan Prediction

29.Road Traffic Prediction

30.Income Classification

31.Speech Emotion Recognition

32.Celebrity Voice Prediction

33.Store Sales Prediction

34.Detecting Parkinson’s Disease

35.Air Pollution Prediction

36.Age and Gender Detection

37.Optimizing Product Price

38.IMDB Predictions

39.Handwritten Digit Recognition

40.Quora Insincere Questions Classification

41.Driver Drowsiness Detection

42.Web Traffic Time Series Forecasting

43.Survival Prediction on the Titanic

44.Time Series Modelling

45.Image Caption Generator

46.Insurance Purchase Prediction

47.Crime Analysis

48.Customer Segmentation

49.Taxi Trip Time Prediction

50.Job Recommendation System

51.Boston Housing Predictions

52.Sentiment Analysis

53.Interest Level in Rental Properties

54.Keyword generation for Google Ads

55.Employee Computer Access Needs

56.Tweets Classification

57.Movie Recommendation System

58.Product Price Suggestions

59.Brain Tumor Detection with Data Science

60.Forest Fire Prediction

61.Human Action Recognition

62.Generative AI Projects