BCA with Specialization in Artificial Intelligence with 100% Placement(Bundle)
MCA with Specialization in Artificial Intelligence : A 2-year course offering Good Job Package in 1st year. Gain NAAC A+ degree while developing expertise in Ai. Start your career in the thriving Artificial Intelligence industry.
Who should enroll in BCA with Specialization in Artificial Intelligence with 100% Placement?
Students, professionals, Data Scientists, Machine Learning enthusiast, Researchers and academicians and Tech professionals seeking to upgrade their knowledge in Ai and Python for career enhancement.
Why Should You Learn This course?
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Future relevance
Abundant career opportunities -
Societal impact
Automation and efficiency -
Innovation and problem-solving
Personal and professional growth
Hiring Company
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What you will learn
- Introduction to Artificial Intelligence
- Artificial Intelligence vs Machine Learning
- Linear Regression using Python
- Multiple Linear Regression
- Evaluation Metrics For Classification
- Machine Learning random forest algorithm
- Clustering algorithms in Machine Learning
- Clustering vs Classification
- Recommender system in Machine Learning
- Machine Learning Projects
- Decoding Model selection for Machine Learning
- Machine Learning Model Deployment using Flask
- Practical Ai Project experience
Technologies you'll Master
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Project
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This project refers to detect whether a person is wearing a mask or not.
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In this project we will use tensorflow framework created by Google for creating Deep Learning models.
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In this project we build our own plagiarism checker to search vast database for stolen content.
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In this Project we build a chatbot program that simulates human conversation or "chatter" through text or voice interactions.
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This project aimed to predict breast cancer using different machine-learning approaches.
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This project takes parameters such as temperature, humidity, and wind to forecast weather.
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This project use a set of different strategies and algorithms to help users find the most relevant films.
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This project will apply Machine Learning algorithm and Linear Regression to predict Healthcare Insurance costs.
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This project refers to detect whether a person is wearing a mask or not.
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In this project we will use tensorflow framework created by Google for creating Deep Learning models.
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In this project we build our own plagiarism checker to search vast database for stolen content.
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In this Project we build a chatbot program that simulates human conversation or "chatter" through text or voice interactions.
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This project aimed to predict breast cancer using different machine-learning approaches.
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This project takes parameters such as temperature, humidity, and wind to forecast weather.
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This project use a set of different strategies and algorithms to help users find the most relevant films.
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This project will apply Machine Learning algorithm and Linear Regression to predict Healthcare Insurance costs.
Course Journey
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Practical Training Overview
The first part of the course journey focuses on providing students with comprehensive practical training to develop their skills and competencies in real-world scenarios. This hands-on experience allows students to apply theoretical knowledge gained in the classroom to practical situations of real world. Time period : 11 months (1st year)Internship Experience
In the second part of the course journey, students have the opportunity to undertake internships in various companies ranging from startups to established companies, gaining insights into diverse industry sectors which provide a direction for students to enhance their technical & soft skills.Time period : 13 months (2nd year)
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100% Placement in Big Companies
We Perfect eLearning offers dedicated support to students during the placement process. This may include career counseling, resume building, interview preparation, and networking opportunities. Students are equipped with the necessary skills to showcase their abilities to potential employers in Final yearProgram outcomes
- Advanced problem-solving skills..
- Expertise in machine learning and deep learning..
- Natural Language Processing (NLP) skills.
- Competence in computer vision and abilities in reinforcement learning.
- Practical AI project experience..
- Proficiency in Python programming & Ai Ethics..
Who will teach?
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Perfect eLearning
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Perfect e-learning is tech-enabled education platform. Here we help students and working professionals to improve their skill sets as per the requirements of the industry. Our instructors are from Google, Adobe, Flipkart, and Amazon.
Here we provide many courses like Python, Data Science, Web Development, Block Chain, Android Development Applications, Digital Marketing, Sales & Marketing, etc. Perfect e-learning provides 100% guaranteed internship and placement support.
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- Why Python
- Preparing your Machine (PC) for Python
- First Python Project & Assignment
- Constants, Reserved Words and Variables
- Module Assignment Solutions: Expressions and User
- Constants in Python: Introduction, application and use cases
- Variable Reserved Words in Python: Introduction, application and use cases
- Data Types in Python
- Conversions in Python: Type and string conversions
- Taking user inputs in Python: Introduction and application
- Module Quiz: Constant Resever Words Variables
- Constants Reserved Words Variables Assignment
- Module Assignment Solution: Expressions and User inputs
- Conditions And Comparisons: Introduction and Use Cases
- Functions and Code Reuse in Python
- Understanding Functions in Python
- Understanding Functions with Arguments in Python
- Return Functions: Introduction and use cases
- Multiple Arguments in Python: Introduction and use cases
- Module Quiz: Functions in Python
- Module Assignment: Functions in Python
- Module Assignment Solution: Functions in Python
- Loops and Iterations in Python
- Loops in Python: Introduction
- Break Continue: Understanding and Use Cases
- FOR Loop: Understanding and Use Cases
- Demo: Loops Code Implmentation
- Loops and interation
- Demo: Mastering Loops with more Use Cases
- Module Quiz: Loops in Python
- Module Assignment: Loops in Python
- Module Assignment Solution: Loops in Python
- BONUS VIDEOS
- Live Classes: Python
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- Introduction to Strings and Functions
- Introduction Video: Data Structures Course Preview
- Introduction to Strings and LEN function Preview
- String Looping: Introduction and Use Cases
- String Slicing: Introduction and Use Cases
- String Library Function: Introduction and Use Cases
- Stripping Whitespace: Introduction and Use Cases
- Parsing Extracting
- Module Quiz: Strings in Python
- Module Assignment: Strings in Python
- Module Assignment Solution: Strings in Python
- Handling Files in Python
- Introduction to Files in Python
- Counting Lines in a File in Python: Introduction and use cases
- Reading files in Python: Introduction and use cases
- Searching Lines in a File in Python: Introduction and use cases
- Naming Files with User Data: Introduction and use cases
- Module Quiz: Files in Python
- Module Assignment: Handling files in Python
- Module Assignment Solution: Handling files in Python
- Lists in Python
- Introduction to Lists in Python
- Finding Length of Range Objects in Python: len()
- Continuation of lists and their use cases
- Calculating Sum and Average of Lists in Python
- Spliting Strings in to Lists in Python: split()
- Moduel Quiz: Lists
- Module Assignment: Lists in Python
- Module Assignment Solution: Lists in Python
- Dictionaries in Python
- Understanding Dictionaries in Python
- Distinguishing Lists and Dictionaries in Python: Lists Vs. Dictionaries
- Demo: Understanding Get Function in Python
- Iterating over dictionaries with loops
- Module Quiz: Dictionaries in Python
- Module Assignment: Dictionaries in Python
- Module Assignment Solution: Dictionaries in Python
- Tuples in Python
- Sets in Python
- Uderstanding Sets in Python
- Creating Sets in Python
- Creating Empty Dictionaries in Python: Empty Set
- Understanding Add and Update in Sets
- Deleting Elements of Sets in Python
- Distinguishing Sets, Lists and Dictionaries in Python: Sets Vs. Lists Vs. Dictionaries
- Python Set Operations (Union, Intersection, Difference and Symmetric Difference)
- Understanding Python Set Methods
- Module Quiz: Sets in Python
- Module Assignment: Python Sets
- BONUS VIDEOS
- Live Classes: Data Structures
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- Machine Learning Introduction
- Regression in Python
- Introduction to Regression
- Various Regression Techniques: Linear, Polynomial, Stepwise, Ridge, Lasso and ElasticNet
- Linear Regression using Python
- Linear Regression Techniques: Slopes And Intercept
- Linear Regression using Sklearn Coding
- Linear Regression Demo using Scikit Lean Code Link
- Demo Code Explaination: Linear Regression using SKLEARN Coding
- Errors in Linear Techniques
- R-Squared or Coefficient of Determination
- Multiple Linear Regression (MLR)
- Polynomial Vs. Non Linear Regression
- Module Quiz: Regression in Python
- Machine Learning with Python: Classification
- Introduction to Classification
- K-Nearest Neighbor(KNN) Algorithm for Machine Learning Introduction
- Working with K-Nearest Neighbor(KNN) Algorithm for Machine Learning
- Evaluation Metrics for Classification
- Machine Learning Decision Tree Classification Algorithm
- Support Vector Machine (SVM) Algorithm in Machine Learning
- Machine Learning Random Forest Algorithm
- Module Quiz: Classification
- Clustering or Cluster Analysis in Machine Learning
- Recommender Systems in Machine Learning
- Python Project 1( Beginner Level): Building Your Own Guess Game
- Python Project 2 ( Intermediate Level): Playing with Graphs (Data Science Project)
- Python Project 3 (Advanced Level): Machine Learning (Deep Learning Project With Tensorflow)
- Live Class: Machine Learning
- Python Capstone Projects
- Machine Learning Workflow Optimization Techniques
- Understanding Machine Learning Workflow
- Decoding Model Selection for Machine Learning
- Understanding Overfitting in Machine Learning
- Understanding Underfitting in Machine Learning
- Understanding the Bias-Variance Tradeoff
- Introduction to Dimensionality Reduction for Machine Learning
- Gradient Descent algorithm and its Variants
- Machine Learning Model Deployment
- Developer Showcase: GitHub
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- Course Introduction
- Breaking down the steps involved in a Data Science project
- Getting System Ready
- Python Basics
- Numpy and Pandas Tutorial
- Data Collection
- Data Visualization and Analysis
- Data Preprocessing
- Machine Learning Basics
- Data Science Projects
- Data Science Projects - Overview
- Breast Cancer Prediction - Overview
- Breast Cancer Prediction - Logistic Regression - Part 1
- Breast Cancer Prediction - Part 2
- Iris Clustering
- Spam mail prediction
- Movie Recommendation System - Overview
- Movie Recommendation System - Part 1
- Movie Recommendation System - Part 2
- Assignment 6 - Data Science projects