WhatsApp

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

+ Free Subscription of

Free Subscription

Craw Security Organizations rely significantly on data in today’s data-driven environment to inform decisions, find creative solutions to challenges, and drive innovation. Thus, there is a growing demand for experts in data science. A thorough 6-month diploma in data science is available from Craw Security, and it’s intended to provide you with the skills and knowledge you need to succeed in this fast-paced industry. Three major topics are covered in this diploma: machine learning, artificial intelligence, and Python.

Certified Curriculum

Industry-aligned modules like Ethical Hacking, Networking, and Pentesting.

Hands-On Labs

Real-world simulations and attack scenarios.

For Beginners & Graduates

No prior experience required.

Placement Assistance

Resume help, mock interviews & job alerts.

1900+ Reviews

Download Course ContentChat on WhatsApp

Craw Security High-End Learning Labs

Lab1
Lab2
Lab3
Lab4
Lab5
Lab6
Lab7
Lab8
Lab9

Placed Students

Tannu

Tannu

Placed in Aguna Solution pvt ltd

Satyam Kumar

Satyam Kumar

Placed in Aguna Solution pvt ltd

Sumit Chaudhary

Sumit Chaudhary

Placed in Cloud Infotech Pvt Ltd

Mohammad Azhar

Mohammad Azhar

Placed in Cloud Infotech Pvt Ltd

Aanchal

Aanchal

Placed in In2 it Technologies

Avinash Verma

Avinash Verma

Placed in idk2 Networks

Sudiksha Gulati

Sudiksha Gulati

Placed in Infocus IT Solution pvt ltd

Mohammad Sameer

Mohammad Sameer

Placed in Ewebguru

Harman Singh

Harman Singh

Placed in Cyber assure services pvt ltd

Purvang

Purvang

Placed in Cyber assure services pvt ltd

Mukund K, jha

Mukund K, jha

Placed in Cyber assure services pvt ltd

Shubham Singh

Shubham Singh

Placed in Nuvam Labs pvt ltd

Tanuj Sharma

Tanuj Sharma

Placed in Nuvam Labs pvt ltd

Ashutosh Chauhan

Ashutosh Chauhan

Placed in Algonauts Advisory Services

Satyam Kumar

Satyam Kumar

Placed in Aguna Solution pvt ltd

Mukund K, jha

Mukund K, jha

Placed in Wi-jungle

Mohit Dev

Mohit Dev

Placed in Wi-jungle

Shubham Gupta

Shubham Gupta

Placed in Wi-jungle

Adarsh Kumar

Adarsh Kumar

Placed in Wi-jungle

Nabeel Ahmed

Nabeel Ahmed

Placed in Wi-jungle

Ravinder Rajput

Ravinder Rajput

Placed in Cyber assure services pvt ltd

Maihar Kumar

Maihar Kumar

Placed in Cyber assure services pvt ltd

Arman Nagpal

Arman Nagpal

Placed in Cyber assure services pvt ltd

Diwakar Singh

Diwakar Singh

Placed in Cyber assure services pvt ltd

Sandeep Rajput

Sandeep Rajput

Placed in Cyber assure services pvt ltd

Mukund K, jha

Mukund K, jha

Placed in Cyber assure services pvt ltd

Shahnawaz

Shahnawaz

Placed in RSM Consulting pvt ltd

Anurag

Anurag

Placed in Techbridge Consultancy pvt ltd

Mukul Meena

Mukul Meena

Placed in ProExcel Technologies pvt ltd

Ravinder Rajput

Ravinder Rajput

Placed in RSC Technologies pvt ltd

What Will You Learn in Six-Months Diploma in Artificial Intelligence (AI) and Machine Learning?

Learners with a good understanding of doing something great with numbers, insights, model building, and analysis can seek their bright future in this highly booming domain of Data Science Diploma with AI through the world-class training faculties at Craw Security. A learning aspirant will sincerely have the best learning environment at Craw Security so that one can study through the most curated training environment.

In this magnificent field of Data Science, one will have a 6-Months Diploma in Data Science in which one will learn the following:

  • Artificial Intelligence
  • Machine Learning
  • Data Analysis
  • Big Data
  • Data Visualization
AI Student

Top Courses in Machine Learning

In the globally-recognized facilities of Craw Security at Saket and Laxmi Nagar locations in New Delhi, there are several courses in Artificial Intelligence in its cluster that make it better to grab an efficient job in the market in the long run. Moreover, these primetime courses in the cluster of Artificial Intelligence are as follows:

Python Programming for Data Science⌄

Python is the foundation upon which modern data science is built. This course provides a solid foundation in Python programming, covering:

  1. Introduction
    1. Programming language introduction
    2. Translators (Compiler, Interpreter)
    3. Uses of computer programs
    4. Algorithm
    5. Flow chart
  2. Python Introduction
    1. History
    2. Why Python was created
    3. Fields of use
    4. Use of Python in Cybersecurity
    5. Reasons for using Python
    6. Syntax
    7. Installation of IDE
  3. Variables
    1. History
    2. What is variable
    3. Declaration rules
    4. Multiple variable declarations
    5. Valid and invalid variables
    6. Type casting
  4. Data Type
    1. Introduction
    2. Discuss all data types
    3. Use type() to show dynamically typed language
    4. String
    5. List
    6. List: List Comprehension
    7. Tuple
    8. Dictionary
    9. Set
  5. Operators
    1. Introduction
    2. Arithmetic operators
    3. Assignment operators
    4. Comparison operators
    5. Logical operators
    6. Identity operator
    7. Bitwise operator
    8. Membership operator
  6. Control Flow
    1. Introduction to Conditional Statement
    2. Conditional Statement: if
    3. Conditional Statement: elif
    4. Conditional Statement: else
    5. Conditional Statement: Nested if
    6. Introduction to Looping
    7. Looping: for loop
    8. Looping: While loop
    9. Looping: Nested loop
  7. Function
    1. Introduction function
    2. Declaration, calling of function
    3. Lambda function
    4. Filter
    5. Reduce function
    6. Map function
  8. File Handling
    1. Introduction
    2. Text file handling
    3. Binary file handling
  9. Object Oriented Programming
    1. Introduction
    2. Difference b/w procedural programming and OOPS
    3. Class
    4. Object
    5. Encapsulation
    6. Inheritance
    7. Abstraction
    8. Polymorphism
  10. Web Scrapping
    1. Introduction
    2. Introduce basic HTML tags
    3. Introduction to Requests Library
    4. Introduction to bs4
    5. Scrapping through Beautiful Soup
  11. Numpy
    1. Creating NumPy arrays
    2. Properties of Array
    3. Indexing and Slicing
    4. Aggregate Functions
    5. Numpy Functions
    6. Vectorization
    7. Broadcasting
    8. Boolean indexing
  12. Pandas
    1. Series
    2. Data Frame
    3. Data Frame Properties
    4. Data Frame indexing and slicing
    5. Reading data from various sources
    6. Dataframe Functions
    7. Pandas Functions
    8. Filter Data
  13. Visualization
    1. Introduction to Matplolib and Seaborn
    2. Properties of plots
    3. Line plot
    4. Histogram / Distplot
    5. Bar plot/ Count Plot
    6. Pie Chart
    7. Heat Map
    8. Scatter Plot
    9. Box Plot
Artificial Intelligence⌄

Artificial intelligence (AI) is causing a revolution in a variety of sectors all over the world, including the healthcare and financial sectors. This course will provide you with an introduction to the field of artificial intelligence and the various applications of this field. The following subjects are discussed:

  1. Artificial Neural Network and Regularization
    1. Single layered ANN
    2. Multiple Layered ANN
    3. Vanishing Gradient problem
    4. Dropout
  2. Introduction to Deep Learning
    1. Difference between ML, DL, and AI
    2. Activation functions
    3. Gradient Descent
  3. Computer Vision & OpenCV
    1. What is Computer Vision
    2. History of Computer Vision
    3. Tools & Technology used in Computer Vision
    4. Application of Computer Vision
    5. What is OpenCV
    6. Installation of OpenCV
    7. The first program with OpenCV
    8. Reading & Writing Images
    9. Capture Videos from Camera
    10. Reading & Saving Videos
  4. Image Classification
    1. Haar Cascade Classifier
    2. Image Classification with CNN
  5. Object Detection
    1. What is Object Detection
    2. Object Detection using Haar Cascade
  6. Introduction to NLP
    1. What is Natural Language Processing
    2. Uses of NLP
    3. Application of NLP
    4. Components of NLP
    5. Stages of NLP
    6. Chatbot
  7. Text Preprocessing
    1. Tokenization
    2. Non-Alphabets Removal
    3. Bag of Words
    4. Stemming & Lemmatization
  8. Sentiment Analysis
    1. What is Sentiment Analysis
    2. Challenges in Sentiment Analysis
    3. Handling Emotions
    4. Sentiment Analysis with ANN
  9. Sequence Model
    1. Sequential Data
    2. Recurrent Neural Network
    3. Architecture of RNN
    4. Vanishing Gradient Problem in RNN
    5. Long Short-Term Memory
Machine Learning⌄

Machine learning is the driving force behind artificial intelligence, and it is causing a shift in the way that businesses analyze and respond to data. This lesson will walk you through the fundamentals of machine learning as well as the algorithms that underpin it, including the following:

  1. Welcome to the ML experience
    1. Importance of ML in your career
    2. AI FAMILY TREE
    3. System requirements
    4. Prerequisites
  2. Machine learning basics
    1. What is machine learning
    2. Classification and regression
    3. Supervised and Unsupervised
    4. Preparing for your ML journey
  3. EDA and Preprocessing
    1. Reading/Writing Excel, CSV, and Other File Formats
    2. Basic EDA (Info, Shape, Describe)
    3. Handling Missing Values
    4. Handling Outliers
    5. Handling Skewness
    6. Encoding Categorical Data (One-Hot, Label Encoding)
    7. Data Normalization and Scaling (MinMax, Standard Scaler)
    8. Feature Engineering
    9. Correlation Analysis and Heatmaps
    10. Train-Test Split & Cross-validation Strategy
  4. Introduction to Regression
    1. Simple Linear Regression
    2. Multiple Linear Regression
    3. Lost and Cost Function (Mean Squared Error)
    4. Regression Evaluation Metrics
    5. Assumptions of Linear Regression
    6. Polynomial Regression
  5. Regularization
    1. Overfitting vs Underfitting
    2. Bias Variance trade-off
    3. Ridge and Lasso Regularization
    4. Cross Validation
  6. Introduction to Classification
    1. Introduction to Logistic Regression
    2. Model Evaluation: Accuracy, Precision & Recall
    3. Model Evaluation: F1 Score, Confusion Matrix
    4. SVM
    5. Decision Tree
  7. Ensemble Learning
    1. What is Ensemble Learning
    2. Bagging
    3. Random Forest
    4. Introduction to Boosting
    5. Boosting: Adaboost
    6. Boosting: Gradient Boost
    7. Boosting: XG Boost
  8. Introduction to Hyperparameter Tuning
    1. Hyperparameter Tuning: GridsearchCV
    2. Hyperparameter Tuning: RandomizedSearchCV
    3. Model Selection Guide
    4. Selecting the Right Evaluation
  9. Unsupervised ML
    1. Introduction to Clustering
    2. K-Means Clustering
    3. Principal Component Analysis

Frequently Asked Questions