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The era of robots and technology is growing, so stay ahead in your career—enroll in our AI & ML course now for better opportunities.

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Learn by Doing & Collaborating, Not Just Watching

Project-Centric Modules

Learn by doing, not just theory – for real-world applicability.

Industry-Relevant Curriculum

Prepare for work with a curriculum that meets real-world demands.

Personalized Feedback

Get personalized feedback to improve every step of the way.

Peer Collaboration

Collaborate with peers to boost understanding and succeed as a team.

Mentorship Program

Mentors support you through challenges, smoothing your learning journey.

Career-Ready Portfolio

Stand out to employers by showcasing your skills for a ready-to-go job hunt.

Get to Know These Skills and Tools as You Learn

As you go through the data science and analytics course, our mentor supports you with skills and tools, keeping you ahead.

Programming

Write and debug code proficiently.

Statistics

Analyze and interpret data effectively.

Machine Learning

Predict patterns with algorithms easily.

Data Processing

Manage and organize information with ease.

Deep Learning

Analyze complex data in a simple way.

Algorithm Design

Create efficient processes.

Problem Solving

Strategically find solutions.

Ethical AI

Weigh moral aspects in AI applications.

Customized Learning for Every Aspiring Pro

Whether you’re working, studying, or seeking career growth, our AI & ML course provides practical skills in an easy-to-understand format.

Your Common Challenges

Our Customized Solutions

Your Common Challenges

Our Customized Solutions

Your Common Challenges

Our Customized Solutions

Your Common Challenges

Our Customized Solutions

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Start your learning adventure in just a few minutes with a simple and speedy sign-up process. It’s easy – just sign up, and you’re ready to dive into the world of knowledge!

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Sign up and start the course in a minutes.

Learn

Learn new things from interesting lessons.

Experience

Do hands-on exercises to really learn & grow.

Get Job Ready

Equip yourself for a job with useful expertise.

Meet Your Instructor

Uttam Grade

Data Scientist 

Aqib Ahmed

ML Engineer

Premalatha T.

Data Scientist/ML Expert

Indrajeet Kumar

Full Stack Developer

Ankit Deshmukh

Sr. Software Engineer

What You'll Learn

Advance excel - Module 1
  • Advanced Formulas and Functions
  • Data Analysis and Visualization
  • Advanced Data Manipulation Techniques
  • Power Query and Power Pivot
  • Macros and VBA Programming
  • Data Integration and External Data Sources
  • Advanced Charting and Visualizations
  • Excel Solver for optimization and what-if analysis…and more.
  • Introduction to Power BI
  • Data Source Connectivity
  • Data Transformation and Modeling
  • Data Visualization
  • DAX (Data Analysis Expressions)
  • Power Query
  • Advanced Data Modeling
  • Measures and KPIs
  • Advanced Visualizations…and more.
  • What is Statistics?
  • Descriptive and Inferential Statistics
  • Types of Data
  • Measures of Central Tendency
  • Mean
  • Median
  • Mode
  • Comparison between Mean, Median and Mode
  • Measures of Dispersion…and more.
  • What is Data Science?
  • What is Machine Learning?
  • Linear Regression
  • Linear Equation- Slope Intercept
  • R square value
  • Regression
  • MSE
  • RMSE
  • MAPE…and more.
  • Introduction to Deep Learning
  • Neural Networks Fundamentals
  • Activation Functions
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Deep Learning for Natural Language Processing (NLP)
  • Transfer Learning
  • Autoencoders
  • Generative Adversarial Networks (GANs)
  • Practical Applications and Case Studies in Deep Learning
  • Introduction to Artificial Neural Networks (ANN)
  • Perceptrons and Multilayer Perceptrons (MLPs)
  • Activation Functions in ANNs
  • Backpropagation Algorithm
  • Training Neural Networks
  • Overfitting and Regularization
  • Optimization Techniques in ANNs
  • Introduction to Deep Learning with ANNs
  • Practical Implementation of ANNs
  • Case Studies and Applications of Artificial Neural Networks
  • Introduction to Keras: A High-Level Neural Networks API
  • Keras Basics: Sequential Models and Functional API
  • Building Neural Networks with Keras
  • Keras Layers and Activation Functions
  • Training Models with Keras
  • Optimizers and Loss Functions in Keras
  • Fine-Tuning and Transfer Learning with Keras
  • Callbacks and Model Evaluation in Keras
  • Hyperparameter Tuning in Keras
  • Practical Projects using Keras
  • Introduction to TensorFlow 2.0
  • TensorFlow Basics: Tensors and Operations
  • Building and Training Simple Neural Networks with TensorFlow
  • TensorFlow Keras API
  • Convolutional Neural Networks (CNNs) in TensorFlow
  • Recurrent Neural Networks (RNNs) in TensorFlow
  • Transfer Learning with TensorFlow
  • TensorFlow 2.0 and Eager Execution
  • Customizing Models with TensorFlow 2.0
  • Deploying TensorFlow Models and Practical Applications
  • Introduction to Convolutional Neural Networks (CNNs)
  • Convolution and Pooling Operations in CNNs
  • CNN Architectures: LeNet, AlexNet, VGG, and ResNet
  • Transfer Learning with CNNs
  • Object Detection with CNNs
  • Image Classification using CNNs
  • CNNs for Facial Recognition
  • Semantic Segmentation with CNNs
  • Practical Implementation of CNNs
  • Case Studies and Applications of Convolutional Neural Networks
  • Introduction to Recurrent Neural Networks (RNNs)
  • Understanding Sequential Data and Time Series
  • RNN Architecture: Basic Cells and Variants
  • Long Short-Term Memory (LSTM) Networks
  • Gated Recurrent Unit (GRU) Networks
  • Bidirectional RNNs
  • Applications of RNNs in Natural Language Processing (NLP)
  • Time Series Prediction with RNNs
  • Sequence-to-Sequence Models with RNNs
  • Practical Implementation and Case Studies of Recurrent Neural Networks
  • Foundations of Artificial Intelligence
  • Introduction to Natural Language Processing (NLP)
  • NLP Techniques: Tokenization, POS Tagging, and Named Entity Recognition
  • Text Representation with Word Embeddings
  • Sentiment Analysis and Text Classification
  • Introduction to Computer Vision
  • Image Processing Techniques
  • Convolutional Neural Networks (CNNs) for Image Classification
  • Object Detection and Image Segmentation in Computer Vision
  • Integration of NLP and Computer Vision in AI Applications
  • Foundations of Artificial Intelligence
  • Introduction to Natural Language Processing (NLP)
  • NLP Techniques: Tokenization, POS Tagging, and Named Entity Recognition
  • Text Representation with Word Embeddings
  • Sentiment Analysis and Text Classification
  • Introduction to Computer Vision
  • Image Processing Techniques
  • Convolutional Neural Networks (CNNs) for Image Classification
  • Object Detection and Image Segmentation in Computer Vision
  • Integration of NLP and Computer Vision in AI Applications
  • Introduction to Computer Vision
  • Image Processing Basics
  • Feature Extraction
  • Image Segmentation
  • Object Detection
  • Image Classification
  • Object Recognition
  • 3D Computer Vision
  • Deep Learning for Computer Vision
  • Advanced Topics
  • Applications of Computer Vision
  • Ethical Considerations in Computer Vision
  • Deep Generative Models
  • Semantic Segmentation
  • Instance Segmentation
  • Attention Mechanisms in Vision
  • Visual Question Answering (VQA)
  • Domain Adaptation in Computer Vision
  • Transfer Learning for Specialized Tasks
  • Few-Shot Learning in Vision
  • Explainable AI in Computer Vision
  • Video Analysis and Action Recognition
  • Unsupervised Learning in Computer Vision
  • Robustness and Adversarial Attacks in Vision Models
  • Real-time Computer Vision Applications
  • Trends and Future Directions in Advanced Computer Vision

See, What Our Learners Are Saying

“As a beginner, I found this AI & ML Course super accessible. Concepts were explained so well!”

Michael Thompson

“Awesome ai & machine learning  course! Even for non-techies like me, it was easy to grasp. Thumbs up!

Emily Johnson

“Fantastic content! Practical examples made learning AI & ML feel like a breeze.”

Liam Parker

“Really liked this course! It explained AI & ML in a way that felt like a conversation, not a lecture. Very helpful!”

Nomsa Molefe

Course Completed – What's in Store for You?

Earn Industry-Recognized Certificates Upon Completion!

Validate your newly acquired skills and boost your credibility as a qualified candidate in the eyes of potential employers. Earn industry-recognized certificates upon course completion.

Be a Part of Our Growing Community!

When you enroll in our course, we want to make you feel like part of our community right from the start. Get our exclusive merchandise delivered straight to your doorstep and stay motivated throughout your learning journey with our creative and high-quality gifts.

Experience the full benefits of being part of our growing community.

Industry Trends

AI and machine learning are at the forefront of technological advancement, and their impact on our world is more profound than ever before. 

64%

Almost two-thirds (64%) of businesses anticipate that AI will make their work more efficient.

$407 Bln.

The global AI market size is expected to reach $407 billion by 2027, with an estimated revenue of $86.9 billion in 2022

21%

AI is expected to contribute a significant 21% net increase to the United States GDP by 2030.

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Frequently Asked Questions

At YHills Edutech, we believe in providing excellent customer service, ensuring that all your queries are answered promptly and efficiently.
Is this course suitable for beginners with no prior experience in machine learning or AI?

 Yes, absolutely! This course is designed for beginners, and we cover the fundamentals before diving into advanced topics.

No prerequisites are required. We start from the basics and gradually progress to more advanced concepts.

Begin with online courses from platforms like YHills,Coursera, edX, or Khan Academy. There are also many free resources to get you started.

Begin with online courses from platforms like YHills,Coursera, edX, or Khan Academy. There are also many free resources to get you started.

Yes, communities like Reddit’s r/MachineLearning, Stack Overflow, and AI-specific forums provide platforms for learning, asking questions, and networking.

Yes, we regularly update the content to reflect the latest industry trends and advancements.

Close Value of Stock Prediction using Neural Network

Idea: Develop a stock prediction model using Neural Network for forecasting the closing value of stocks.

Features:

  • Historical stock price data
  • Neural network architecture for regression
  • Time-based features for time-series prediction

Predictive Maintenance for Industrial Equipment

Objective: Develop a predictive maintenance model using Time-Series Forecasting and Anomaly Detection to anticipate equipment failures and reduce downtime.

Features:

  • Sensor data analysis
  • Time-series forecasting
  • Anomaly detection

Bank Credit Card Default Prediction using ANN on Keras

Idea: Develop a credit card default prediction model using Artificial Neural Network (ANN) with Keras to identify potential defaults in bank credit card transactions.

Features:

  • Credit card transaction data.

  • ANN architecture for classification.

  • Historical default and non-default labels for training.

     

Sentiment Analysis on Customer Reviews

Idea: Analyze customer reviews to understand sentiment and feedback about products or services.

Features:

  • Natural Language.
  • Processing (NLP).
  • Text sentiment analysis.
  • Visualization of sentiment trends.

Image Classification with Keras and TensorFlow

Idea: Implement an image classification model using Keras and TensorFlow to accurately classify images into predefined categories.

Features:

  • Image dataset for training and testing.

  • Convolutional Neural Network (CNN) architecture.

  • TensorFlow and Keras libraries for building and training the model.

Customer Churn Prediction in Telecom Industry

Idea: Build a model using Customer Segmentation and Predictive Modeling to predict customer churn and identify influencing factors.

Features:

  • Customer demographics analysis.
  • Call and usage pattern analysis.
  • Predictive modeling techniques.

Classification of Dogs and Cats Images using CNN

Idea: Build a convolutional neural network (CNN) model to classify images as either dogs or cats, utilizing the power of deep learning for image classification.

Features:

  • Dataset containing labeled images of dogs and cats.
  • Convolutional Neural Network (CNN) architecture tailored for image classification.
  • Image preprocessing techniques for feature extraction and representation in the CNN model.

Stock Price Prediction using Machine Learning

Idea: Utilize Time-Series Analysis, Sentiment Analysis, and Technical Indicators for predicting stock prices and developing effective trading strategies.

Features:

  • Time-series analysis.
  • Technical indicators calculation.

Textual Document Classification

Idea: Develop a model for classifying textual documents into predefined categories using natural language processing techniques.

Features:

  • Textual dataset containing documents with labeled categories.
  • Natural Language Processing (NLP) techniques for text preprocessing.
  • Classification algorithm (such as a machine learning classifier or a deep learning model) to assign documents to appropriate categories based on their content.

Credit Scoring Model

Idea: Develop a credit scoring model to predict the creditworthiness of applicants for loans or credit cards.

Features:

  • Historical credit data analysis.
  • Feature engineering for financial indicators.
  • Ensemble learning techniques.