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Curriculam Designed By Experts
Expertly designed curriculum for future-ready professionals.
Industry Oriented Curriculam
An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company
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30+ Case Studies & Projects
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9+ Engaging Projects
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10+ Years Of Experience
Machine Learning Training Projects
Become a Machine Learning Expert With Practical and Engaging Projects.
- Practice essential Tools
- Designed by Industry experts
- Get Real-world Experience
Iris Classification
Use the Iris dataset to classify different species of iris flowers based on their features. Classification, Data Preprocessing, and Model Evaluation.
Titanic Survival Prediction
Predict whether passengers survived the Titanic disaster based on features like age, sex, and passenger class. Classification, Data Cleaning, and Feature Engineering.
Handwritten Digit Recognition
Build a model to recognize handwritten digits from the MNIST dataset. Image Classification, Neural Networks, and Data Augmentation.
Spam Email Detection
Create a model to use natural language processing techniques to categorize emails as spam or not. NLP, Feature Extraction, Text Classification.
Customer Churn Prediction
Predict which customers are likely to leave a service or product based on their usage patterns and demographics. Classification, Feature Engineering, and Model Evaluation.
Facial Emotion Recognition
Develop a model to recognize emotions from facial expressions using image data. Image Classification, Deep Learning, CNNs.
Autonomous Driving Simulation
Build a model to drive a car autonomously in a simulated environment using reinforcement learning. Reinforcement Learning, Simulation, Deep Learning.
Generative Adversarial Networks (GANs) for Image Generation
Create realistic images using GANs, such as generating new artworks or enhancing low-resolution photos. GANs, Deep Learning, Image Synthesis.
Machine Translation System
Develop a neural network model to translate text from one language to another. Sequence-to-Sequence Models, NLP, Deep Learning.
Key Features
Practical Training
Global Certifications
Flexible Timing
Trainer Support
Study Material
Placement Support
Mock Interviews
Resume Building
Batch Schedule
Weekdays Regular (Class 1Hr - 1:30Hrs) / Per Session
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07-Oct-2024 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
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09-Oct-2024 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)
Weekend Regular (Class 3Hrs) / Per Session
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12-Oct-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session
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12-Oct-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
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- Learning strategies that are appropriate and tailored to your company's requirements.
- Live projects guided by instructors are a characteristic of the virtual learning environment.
- The curriculum includes of full-day lectures, practical exercises, and case studies.
Machine Learning Training Overview
Machine Learning Programmer’s Potential Career Paths
Possibilities for career paths as a machine learning programmer include positions as a deep learning engineer (specializing in neural networks and complex models), software engineer (focusing on integrating machine learning solutions into software products), data scientist (analyzing data to produce actionable insights), AI research scientist (conducting advanced research) and machine learning engineer (developing and deploying models).
What Are the Requirements for an Machine Learning Course Course?
- Basic Programming Knowledge: It's imperative to be proficient in programming languages like Python or R because machine learning frequently uses them.
- Mathematics and Statistics: Algorithm development and comprehension require a strong foundation in calculus, statistics, probability, and linear algebra.
- Familiarity with Data Handling: Basic skills in data manipulation and analysis, including knowledge of libraries like Pandas and NumPy, are often required.
- Understanding of Machine Learning Concepts: Some foundational knowledge of machine learning principles, such as supervised and unsupervised learning, can be beneficial.
- Educational Background: A background in computer science, engineering, mathematics, or a related field is usually recommended, though only sometimes mandatory.
- Software Requirements: Access to a computer with necessary software tools, such as Jupyter Notebooks or integrated development environments (IDEs), may be required.
Reasons to Consider Enrolling in Machine Learning Course
Enrolling in a Machine Learning course offers numerous benefits, including gaining in-demand skills that are highly valued across industries such as technology, finance, and healthcare. It provides the opportunity to work on cutting-edge technologies and solve complex problems using data-driven approaches. The course equips you with practical experience in developing and deploying machine learning models, which can enhance your career prospects and open doors to diverse roles in data science and artificial intelligence.
Techniques and Trends in Machine Learning Development
- Deep Learning: Utilizes neural networks with multiple layers to model complex patterns in data, particularly effective for image and speech recognition.
- Transfer Learning: Leverages pre-trained models on large datasets and fine-tune them for specific tasks, reducing the need for extensive data and computational resources.
- Natural Language Processing (NLP): Machine learning is used to understand and generate human language, with advancements in models like GPT and BERT enhancing text analysis and conversational AI.
- Automated Machine Learning (AutoML): Automates processes like hyperparameter tuning and model selection, making the process of creating machine learning models easier.
The Most Recent Machine Learning Tools
Recent advancements in machine learning have introduced several cutting-edge tools and frameworks. Generative models like GPT-4 and diffusion models are pushing the boundaries of creativity and language understanding. Frameworks such as TensorFlow 2. x and PyTorch 2. x continue to evolve, offering more flexibility and performance enhancements. Additionally, AutoML tools, including Google’s AutoML and Microsoft’s Azure Machine Learning, simplify model selection and hyperparameter tuning. Techniques like few-shot learning and transfer learning are also gaining traction, allowing models to adapt quickly with minimal data.
Career Opportunities After Machine Learning
Machine Learning Engineer
A machine learning engineer creates and develops machine learning models and systems to tackle challenging issues. Their main areas of concentration are algorithms, handling massive datasets.
Data Scientist
Data Scientists analyze and interpret complex data to inform strategic decisions. They apply statistical techniques and machine learning algorithms to extract insights and make data-driven recommendations.
AI Research Scientist
AI Research Scientists conduct cutting-edge research to advance the field of artificial intelligence and machine learning. They develop new algorithms, techniques, and models and contribute to academic papers.
Machine Learning Analyst
Machine Learning Analysts focus on applying machine learning techniques to analyze business data and generate actionable insights. They work on predictive modelling.
Deep Learning Engineer
Deep Learning Engineers specialize in designing and implementing deep neural networks for tasks such as image recognition, natural language processing, and autonomous systems.
Business Intelligence (BI) Developer
BI Developers leverage machine learning and data analysis to create data-driven business intelligence solutions. They develop dashboards, reports, and data visualizations.
Skill to Master
Data Preprocessing and Cleaning
Feature Engineering
Algorithm Selection
Model Training and Evaluation
Hyperparameter Tuning
ACross-Validation
Dimensionality Reduction
Model Deployment
Data Visualization
Statistical Analysis
Natural Language Processing (NLP)
Deep Learning Techniques
Tools to Master
TensorFlow
PyTorch
scikit-learn
Keras
XGBoost
LightGBM
H2O.ai
Apache Spark MLlib
RapidMiner
Weka
Microsoft Azure Machine Learning
Google Cloud AI Platform
Learn from certified professionals who are currently working.
Training by
Priya , having 10 yrs of experience
Specialized in:Computer Vision, Image Classification, Object Detection, Generative Adversarial Networks (GANs), and Deep Learning Frameworks.
Note:Priya is an accomplished researcher in computer vision with a strong background in PyTorch and Keras. Her teaching emphasizes practical applications of computer vision technologies, making her ideal for students interested in cutting-edge visual recognition and analysis.
We are proud to have participated in more than 40,000 career transfers globally.
Machine Learning Certification
A machine learning certification does not ensure work, but it can significantly increase your employability and make you a more competitive applicant. Although obtaining a certification can help you land a better job, it also depends on other aspects, including your level of experience, the state of the labour market, and the demands of particular employers.
Yes, you are able to obtain certificates for numerous Machine Learning courses. Actually, doing so can lead to a more comprehensive and in-depth comprehension of the different instruments, methods, and frameworks used in the subject. You can develop a well-rounded skill set and increase your expertise by obtaining various certifications that concentrate on particular platforms, techniques, or advanced topics.