Six-Months Diploma in Artificial Intelligence (AI) and Machine Learning (ML) in Delhi

Python Programming for Data Science

Python serves as the cornerstone of contemporary data science. The goal of this course is to give students a strong foundation in Python programming so they can understand the complexities of data analysis more easily. You will obtain:

Introduction

a) programming language introduction
b) Translators (Compiler, Interpreter)
c) Uses of computer programs
d) Algorithm
e) Flow chart

Python Introduction

a) History
b) Why python created
c) Fields of use
d) Use of Python in Cybersecurity
e) Reasons for using Python
f) Syntax
g) Installation of IDE

Variables

a) What is variable
b) Declaration rules
c) Multiple variable declarations
d) Valid and invalid variables
e) Type casting

Data Type

a) Introduction
b) Discuss all data types
c) Use type() to show dynamically typed language
d) String
e) List
f) List: List Comprehension
g) Tuple
h) Dictionary
i) Set

Operators

a) Introduction
b) Arithmetic operators
c) Assignment operators
d) Comparison operators
e) Logical operators
f) Identity operator
g) Bitwise operator
h) Membership operator

Control Flow

a) Introduction to Conditional Statement
b) Conditional Statement: if
c) Conditional Statement: elif
d) Conditional Statement: else
e) Conditional Statement: Nested if
f) Introduction to Looping
g) Looping: for loop
h) Looping: While loop
i) Looping: Nested loop

Function

a) Introduction function
b) Declaration, calling of function
c) Lambda function
d) Filter
e) Reduce function
f) Map function

File Handling

a) Introduction
b) Text file handling
c) Binary file handling

Object-Oriented Programming

a) Introduction
b) Difference between procedural programming and OOPS
c) Class
d) Object
e) Encapsulation
f)  Inheritance
g) Abstraction
h) Polymorphism

Web Scrapping

a) Introduction
b) Introduce basic HTML tags
c) Introduction to Requests Library
d) Introduction to bs4
e) Scrapping through Beautiful Soup

Numpy

a) Creating NumPy arrays
b) Properties of Array
c) Indexing and Slicing
d) Aggregate Functions
e) Numpy Functions
f) Vectorization
g) Broadcasting
h) Boolean indexing

Pandas

a) Series
b) Data Frame
c) Data Frame Properties
d) Data Frame indexing and slicing
e) Reading data from various sources
f) Dataframe Functions
g) Pandas Functions
h) Filter Data

Visualization

a) Introduction to Matplolib and Seaborn
b) Properties of plots
c) Line plot
d) Histogram/Distplot
e) Bar plot/Count Plot
f) Pie Chart
g) Heat Map
h) Scatter Plot
i) Box Plot

Artificial intelligence is powered by machine learning, which is changing how companies evaluate and react to data. The principles of machine learning and the algorithms that support it will be covered in this lesson, including the following:

Welcome to the ML experience

a)      Importance of ML in your career
b)      AI FAMILY TREE
c)      System requirements
d)      Prerequisites

Machine learning basics

a)      What is machine learning?
b)      Classification and regression
c)      Supervised and Unsupervised
d)      Preparing for your ML journey

EDA and Preprocessing

a)      Reading/Writing Excel, CSV, and Other File Formats
b)      Basic EDA (Info, Shape, Describe)
c)      Handling Missing Values
d)      Handling Outliers
e)      Handling Skewness
f)      Encoding Categorical Data (One-Hot, Label Encoding)
g)      Data Normalization and Scaling (MinMax, Standard Scaler)
h)      Feature Engineering
i)      Correlation Analysis and Heatmaps
j)      Train-Test Split & Cross-validation Strategy

Introduction to Regression

a)      Simple Linear Regression
b)      Multiple Linear Regression
c)      Lost and Cost Function (Mean Squared Error)
d)      Regression Evaluation Metrics
e)      Assumptions of Linear Regression
f)      Polynomial Regression

Regularization

a)      Overfitting vs Underfitting
b)      Bias Variance trade-off
c)      Ridge and Lasso Regularization
d)      Cross Validation

Introduction to Classification

a)      Introduction to Logistic Regression
b)      Model Evaluation: Accuracy, Precision & Recall
c)      Model Evaluation: F1 Score, Confusion Matrix
d)      SVM
e)      Decision Tree

Ensemble Learning

a)      What is Ensemble Learning?
b)      Bagging
c)      Random Forest
d)      Introduction to Boosting
e)      Boosting: Adaboost
f)      Boosting: Gradient Boost
g)      Boosting: XG Boost

Introduction to Hyperparameter Tuning

a)      Hyperparameter Tuning: GridsearchCV
b)      Hyperparameter Tuning: RandomizedSearchCV
c)      Model Selection Guide
d)      Selecting the Right Evaluation

Unsupervised ML

a)      Introduction to Clustering
b)      K-Means Clustering
c)      Principal Component Analysis

Around the world, artificial intelligence (AI) is revolutionizing several industries, including the financial and healthcare sectors. An introduction to artificial intelligence and its diverse applications will be given to you in this course. The following topics are covered:

Artificial Neural Network and Regularization

1. a)      Single-layered ANN
   b)      Multiple Layered ANN
   c)      Vanishing Gradient problem
   d)      Dropout

Introduction to Deep Learning

1. a)      Difference between ML, DL, and AI
   b)      Activation functions
   c)      Gradient Descent

Computer Vision & OpenCV

1. a)      What is Computer Vision?
   b)      History of Computer Vision
   c)      Tools & Technology used in Computer Vision
   d)      Application of Computer Vision
   e)      What is OpenCV?
   f)      Installation of OpenCV
   g)      The first program with OpenCV
   h)      Reading & Writing Images
   i)      Capture Videos from Camera
   j)      Reading & Saving Videos

Image Classification

1. a)      Haar Cascade Classifier
   b)      Image Classification with CNN

Object Detection

1. a)      What is Object Detection
   b)      Object Detection using Haar Cascade

Introduction to NLP

1. a)      What is Natural Language Processing?
   b)      Uses of NLP
   c)      Application of NLP
   d)      Components of NLP
   e)      Stages of NLP
   f)      Chatbot

Text Preprocessing

1. a)      Tokenization
   b)      Non-Alphabets Removal
   c)      Bag of Words
   d)      Stemming & Lemmatization

Sentiment Analysis

1. a)      What is Sentiment Analysis?
   b)      Challenges in Sentiment Analysis
   c)      Handling Emotions
   d)      Sentiment Analysis with ANN

Sequence Model

1. a)      Sequential Data
   b)      Recurrent Neural Network
   c)      Architecture of RNN
   d)      Vanishing Gradient Problem in RNN
   e)      Long Short-Term Memory.

Market Share of Data Science

In 2023, the data science platform market was projected to be worth USD 103.93 billion worldwide. It is anticipated to rise from USD 133.12 billion in 2024 to USD 776.86 billion by 2032 at a compound annual growth rate (CAGR) of 24.7% during the forecast period.

A data science platform is a piece of software that offers a platform for the entire life cycle of a data science project. These platforms are essential resources for data scientists because they facilitate the creation, sharing, and analysis of models.

It also makes data preparation and visualization easier and offers a large-scale computing infrastructure. These systems provide a centralized platform that makes it easier for users to work together.

Why Choose Craw Security to Learn a 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?

.Selecting Craw Security for thorough training in Data Science with AI from highly qualified experts with years of excellent experience can be very helpful for both substantial career growth and respectable personal development.

Before deciding on Craw Security as your ideal partner in this field, you can take into account the following important factors:

Complete freedom to select the learning mode, including:
a)      VILT (Virtual Instructor-Led Training) Sessions
b)      Pre-recorded Video Sessions, and
c)      Offline Classroom Sessions.

Top-notch, highly skilled training staff.
●  Both soft and hard copies of the study materials are available.
●  Verified research materials from data scientists employed by a variety of global organizations.
●  After completing the course and passing an internal exam, students receive a Certificate of Completion.

Market Share of Data Science

In 2023, the data science platform market was projected to be worth USD 103.93 billion worldwide. It is anticipated to rise from USD 133.12 billion in 2024 to USD 776.86 billion by 2032 at a compound annual growth rate (CAGR) of 24.7% during the forecast period.

A data science platform is a piece of software that offers a platform for the entire life cycle of a data science project. These platforms are essential resources for data scientists because they facilitate the creation, sharing, and analysis of models.

It also makes data preparation and visualization easier and offers a large-scale computing infrastructure. These systems provide a centralized platform that makes it easier for users to work together.

Why Choose Craw Security to Learn a 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?

.Selecting Craw Security for thorough training in Data Science with AI from highly qualified experts with years of excellent experience can be very helpful for both substantial career growth and respectable personal development.

Before deciding on Craw Security as your ideal partner in this field, you can take into account the following important factors:

Complete freedom to select the learning mode, including:
a)      VILT (Virtual Instructor-Led Training) Sessions
b)      Pre-recorded Video Sessions, and
c)      Offline Classroom Sessions.

Top-notch, highly skilled training staff.
●  Both soft and hard copies of the study materials are available.
●  Verified research materials from data scientists employed by a variety of global organizations.
●  After completing the course and passing an internal exam, students receive a Certificate of Completion.