Machine Learning Training in Marathahalli
- Machine Learning Specialist with 12+ Years of Certification.
- Cost-effective Machine Learning Courses are Available.
- Customized Support for Machine Learning Job Interviews.
- 362+ Hiring Partners and 13,409+ Trained Individuals.
- Comprehensive Online Materials, Videos, and Interview Guides.
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See why over 25,000+ Students choose ACTE











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 Course Projects
Become a Machine Learning Expert With Practical and Engaging Projects.
House Price Prediction
Use linear regression to predict house prices based on features like size, location, and age, perfect for understanding basic ML algorithms and data handling.
Iris Flower Classification
Implement a simple classification model using the Iris dataset to classify flowers based on petal and sepal dimensions, great for learning data preprocessing and basic classification.
Movie Recommendation
Build a basic recommendation engine using collaborative filtering to suggest movies to users based on their ratings and preferences, introducing recommender systems.
Customer Segmentation
Utilize clustering techniques like K-means to segment customers based on purchase behavior, helping in targeted marketing and understanding unsupervised learning concepts.
Stock Price Prediction
Develop a time series model using LSTM (Long Short-Term Memory) networks to forecast stock prices, enhancing skills in deep learning and sequential data handling.
Spam Email Detection
Create a classification model using Naive Bayes or SVM (Support Vector Machine) to detect spam emails, focusing on text processing, feature extraction, and model evaluation.
Face Recognition Network System
Design a deep learning model using CNNs (Convolutional Neural Networks) for real-time face recognition, emphasizing complex neural network architectures and image processing.
Autonomous Vehicle Navigation
Implement a reinforcement learning model to control the movement of an autonomous vehicle in a simulated environment, exploring advanced control algorithms and real-world ML App.
Fraud Detection in Financial Transactions
Build an anomaly detection model using autoencoders or ensemble methods to identify fraudulent transactions in large datasets, requiring deep knowledge of data mining and pattern recognition.
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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10-Feb-2025 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
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12-Feb-2025 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)
Weekend Regular (Class 3Hrs) / Per Session
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15-Feb-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session
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16-Feb-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Enquiry Form
- 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 Course Overview
Possible careers machine learning programmers may seek.
A Machine learning programmer can have a career in data science where they ascertain massive amounts of information to aid in business decision-making. A machine learning engineer creates and applies machine learning models within systems with operations-related functionalities. An artificial intelligence research scientist examines new technologies and algorithms in furthering the field of study. An AI research scientist would research new algorithms and technologies and then try to bring such progress. They might also work as a Quantitative Analyst in finance, applying machine learning to predictive modelling and risk assessment. They could also develop as a Robotics Engineer, working on robotics development. They might work as Bioinformatics Specialists in healthcare, analyzing medical data. In addition, they may further engage in academic or industry consulting, serving several sectors.
Requirements for a Machine Learning Course
- Fundamental Programming Skills: Fundamental Programming Skills, such as using languages like Python or R, are necessary for implementing machine learning models and algorithms.
- Mathematical Aptitude: Mathematical Aptitude a good knowledge of linear algebra, calculus, and statistics is necessary to understand the concepts and techniques used in machine learning.
- Knowledge of Data Handling: Knowledge of Data Handling Using libraries like Pandas or NumPy, Machine Learning data must be preprocessed and altered in order to prepare datasets for analysis.
- Understanding of Algorithms: Understanding of Algorithms supervised and unsupervised learning are two fundamental machine learning algorithms that are necessary for building and assessing the models.
- Familiarity with Tools and Libraries: Practical implementation involves familiarizing oneself with the associated machine learning frameworks, libraries, and tools, such as TensorFlow, Keras, or Scikit-learn.
- Problem-Solving Skills: Problem-Solving Skills This requires strong analytical and problem-solving skills to develop effective models and deliver accurate interpretations of findings.
Benefits of Enroll in Machine Learning Course
The first benefit of enrolling in a machine learning course is that you will acquire highly in-demand skills in an area that is fast-growing and revolutionizing various sectors. In the process, you tap into the brains of experts, get hands-on experience with datasets and projects, and encounter a wide range of techniques and applications, thereby increasing your capacity to tackle difficult problems. Plus, course platforms provide abundant opportunities for networking between peers and industry professionals, which may help one's career. The skills developed in machine learning will enable you to contribute to innovative projects and solutions.
Methods and Patterns in the Development of Machine Learning
- Deep Learning: Deep learning defines complex patterns in large datasets using multi-layered neural networks, an application that is extensively used for speech and image recognition.
- Transfer Learning: Transfer learning employs pre-trained models to be applied to new but related tasks; Machine learning significantly reduces the time and amount of data required to train.
- Reinforcement Learning: This learning is training models to make decisions by rewarding desired actions and penalizing undesired ones. It had a broad surge in applications related to robotics and game development.
- Natural Language Processing (NLP): Natural Language Processing enhances machines' capacity to understand and create human language. This trend resulted in significant chatbots, translation, and sentiment analysis advancements.
- Explainable AI (XAI): Machine Learning Explainable AI (XAI) focuses on explaining the AutoML/Automated Processing machine learning models in a more understandable and transparent way.
- Machine Learning: dealing with the black-box nature of complex models. AutoML/Automated Machine Learning: Simplifies the ML process by automating model selection, hyperparameter tuning, and feature engineering.
New Advances in Machine Learning Technologies
New technological advancements in machine learning introduced a new kind of supporting technologies. TensorFlow 2.0 offers a more intuitive and enhanced neural network creation and training interface. The flexibility and dynamic computing structure of PyTorch maintain its popularity. Hugging Face Transformers have pre-trained state-of-the-art models for natural language processing tasks. MLflow helps in managing the lifecycle in the application of machine learning by aiding experimentation, replication as well as implementation. KubeFlow augments the scalability and makes deployment easier through connecting Google Colab providing free access to GPU and TPU. This makes sharing and collaborating on a machine-learning project very easy.
Career Opportunities After Machine Learning
Machine Learning Engineer
Designs and develops algorithms to improve predictive models and enhance the accuracy of data-driven decisions. Works on large datasets, builds machine learning models, and deploys them into production .
Data Scientist
Analyzes complex data sets to derive insights and build predictive models. Utilizes statistical methods and machine learning techniques to solve business problems and guide strategic decisions. AI Research Scientist
AI Research Scientist
Conducts cutting-edge research in artificial intelligence and machine learning, developing new algorithms and models. Works on advancing AI technologies and publishing findings in academic journals.
Machine Learning Developer
Creates and integrates machine learning models into software applications. Focuses on optimizing model performance and ensuring seamless deployment within existing systems.
Deep Learning Engineer
Specializes in designing and implementing deep neural networks to solve complex problems such as image and speech recognition. Works with advanced frameworks and large-scale datasets.
Business Intelligence Analyst
Leverages machine learning models to extract actionable insights from data, supporting business strategy and decision-making. Develops dashboards and reports to visualize trends and performance metrics.
Skill to Master
Data Preprocessing and Cleaning
Statistical Analysis and Probability
Supervised and Unsupervised Learning
Model Evaluation and Validation
Feature Engineering and Selection
Algorithm Implementation
Deep Learning and Neural Networks
Programming with Python and R
Data Visualization and Interpretation
Handling Big Data Technologies
Optimization Techniques
Real-World Problem Solving
Tools to Master
TensorFlow
PyTorch
Scikit-Learn
Keras
XGBoost
LightGBM
Apache Spark MLlib
H2O.ai
IBM Watson Studio
Google Colab
Jupyter Notebook
Weka
Learn from certified professionals who are currently working.

Training by
Shweta Jain , having 10 yrs of experience
Specialized in: Machine Learning for Healthcare, Medical Imaging, Predictive Health Analytics, and Bioinformatics.
Note:Shweta focuses on healthcare applications of machine learning, including medical imaging and predictive health analytics. She is skilled in bioinformatics and healthcare data analysis.
We are proud to have participated in more than 40,000 career transfers globally.

Machine Learning Certification

Pursuing a Machine Learning certification enhances your professional credibility, validates your expertise in a specific domain, and increases your employability by showcasing verified skills employers seek. It can also open doors to new career opportunities, higher salaries, and promotions while keeping you updated with industry trends and practices.
Certification does guarantee employment but significantly boosts your chances. It demonstrates a verified skill set, making you more competitive. However, employers also consider experience, soft skills, and cultural fit during hiring.
Some Machine Learning certification exams have prerequisites, such as specific educational qualifications, professional experience, or the completion of prerequisite courses. The requirements vary depending on the Machine Learning certification, so it’s important to review the specific requirements beforehand.
Practical experience is only sometimes required for Machine Learning certification but is highly beneficial. For many advanced Machine Learning certifications, practical experience ensures a deeper understanding of the subject matter and is often part of the eligibility criteria.
An ACTE Machine Learning certification is a valuable investment in your career growth. It enhances your knowledge, builds industry-recognized credentials, and helps you stay competitive in a fast-evolving job market.
Frequently Asked Questions
- You must not wait to register for a sample session to join any Machine Learning course here at ACTE. You can register for a sample session online quickly, and by registering for it, you will be provided with access details in no time. Sample sessions will give you an overview of the course and teaching methodology so that you may be better positioned to decide before enrolling on the entire course.
- The instructors at ACTE are professionals with many years of experience and knowledge of Machine Learning. Certified instructors deliver practical ideas and hands-on training from real-world experiences. These instructors are committed to helping learners understand and master the Machine Learning suite of products, ensuring they gain the skills needed for success in various administrative and corporate roles.
- Resume Building
- Interview Preparation
- Job Search Support
- Career Counseling
- Networking Opportunities
- By the end of the Machine Learning course at ACTE, you will be certified to show your proficiency in using Machine Learning applications. Such a certificate, which testifies that you can work in Word, Excel, PowerPoint, and Outlook, among other Machine Learning applications, is valued much by employers and can always be included in your resume as evidence of your skills. It also brings to light your commitment to upgrading your skills, which makes a person more attractive for prospective employment.
- Real-Life Case Studies
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