Our placement and success stories showcase the achievements of our students and the opportunities that are available
Machine Learning Certificate Course In Pune
- Online and Offline Classes
- Certificate of Completion
- 100% Placement assistance
- Industry focused curriculum
- Timely assignments
- Mock interviews
Unlock lucrative career opportunities in the dynamic field of machine learning by mastering the art through our hands-on course in Pune. Learn important skills and make clever applications to stand out in the tech industry. Go for the machine learning course in Pune by Felix-ITs!
Unlock the Power of Data with Machine Learning Course in Pune
Unlock lucrative career opportunities in the dynamic field of machine learning by mastering the art through our hands-on course in Pune. Learn important skills and make clever applications to stand out in the tech industry. Go for the machine learning course in Pune by Felix-ITs!
Problem solving skills
Design fundamentals
Career advancement
Industry best practices
Career Opportunities
Discover and unleash the potential of your future by exploring the plethora of exciting career opportunities available to you
Machine Learning Engineer
Data Scientist
AI Research Scientist
Big Data Engineer
Robotics Engineer
Master Machine Learning Basics with Our Course in Pune!
1
In-depth training with hands-on application
2
One-on-one mentorship with industry experts
3
Real-world projects for practical learning
4
Theory classes are followed by practical sessions conducted in labs
Curriculum of Machine Learning Course in Pune
Our curriculum is tailored to provide you with a comprehensive understanding of Machine Learning
-
Introduction to Machine Learning
- Python basic Functions and useful common library
- Basic Mathematics required
- Data Manipulation with Pandas
- Visualization in Python: Matplotlib
-
Data Preprocessing Techniques
- Feature selection
- Feature engineering
Features of the Course
4 months curriculum
Get a robust 4-month curriculum that covers all the essentials of the course
Unlimited Mentoring
Get mentoring from industry experts to guide you every step of the way
Build Your Portfolio
Build an impressive portfolio of real-world projects to showcase your skills and stand out from the competition
Placement Support
Get dedicated placement support to help you land a job and kickstart your career
A Student’s Journey
Shape Your Career with Our Comprehensive Journey
Select Project
Choose your project topic and start the project with our project-based learning approach
Complete UI and Create Database
Complete the frontend of your project and prepare its database
Complete Backend
Create API’s required for your project
Get your Project reveiwed
After completing the project, get it approved by your mentor
Upload to Github
Upload your created project on Github
1st project Completed
After uploading, present your project to your mentors and your 1st project will be completed!
Course Highlights
1. What is Machine learning?
Machine learning is a type of artificial intelligence (AI) that lets computers learn without being explicitly programmed. It analyzes data to identify patterns and then uses those patterns to make predictions or decisions on new data. This is used in many things like spam filters and facial recognition software.
2. How to start machine learning?
To start with machine learning, first learn basic programming skills, particularly Python. Then, study introductory ML concepts through online tutorials, and books or by attending machine learning classes in Pune. Enroll in a Machine Learning course in Pune for structured learning, practical exercises, and expert guidance. Practice with datasets, implement algorithms and engage in projects to reinforce understanding.
Student Work
Our student work showcases the real-world application of the skills and techniques you will learn in our course. Get inspired and see what you can achieve with our program
Skylar Levin
Phillip Geidt
Skylar Levin
Phillip Geidt
Our Recent Placements
Ria Deshmukh
Engineer (Pre Felix)
UI/UX Designer (Post Felix)
Working at
Prashanti Nagdeve
Event Manager (Pre Felix)
UI/UX Designer (Post Felix)
Working at
Swati Lodha
Fresher (Pre Felix)
Full Stack (Post Felix)
Working at
We are proud to have trained professionals who have gone to work at some of the biggest names in the industry
Rakhi Vighe
UI/UX
UI/UX
Manish Durgude
UI/UX
UI/UX
One of the standout features of the Felix IT Institute is their job placement assistance. After completing the program, I was able to leverage their extensive network of industry contacts to secure a job in my field. They were with me every step of the way, providing valuable feedback on my portfolio and resume, as well as interview coaching.
Chinmay Nimkar
UI/UX
UI/UX
Originally hailing from travel & tourism sector, pandemic was a difficult time for me. While researching different career opportunities, I came across Felix institute and it truly made a huge impact on my life. I learnt proficient UI UX design under the guidance of their experts and managed to land a job at the prestigious SandCup Design Studio. I am eternally grateful to my mentor Tanmay Sir and Manali ma'am for their massive contribution in my career.
Course Completion Certificate
Award Yourself for Your Hard Work
Proudly display your certificate and show the world what you have accomplished with our program.
Eligibility
If you meet the below criteria, you are eligible to join our comprehensive Machine Learning course in Pune and start your journey to success!
- IT PROFESSIONALS If you are a professional and looking to enhance your profile then this course is the right choice as a first step in upscaling your profile.
- ENTRY LEVEL DEVELOPERS Fresh graduates who are looking to make an entry in IT world this course would be good start to make yourself stand apart from the crowd ,get your fundamentals strong.
Where to Begin Your Journey?
1
Attend our free webinar
Get an introduction to the world of Machine Learning and learn how it can benefit your career. Our expert instructors will guide you through the basics.
Register2
Live session with mentor
Take your learning to the next level with a one-on-one session with a professional Machine Learning.
Book session3
Enroll for the course
Sign up now and get ready to embark on your Machine Learning journey with confidence and support!"
EnrollNew Batch Alert
Are you ready to start your Machine Learning journey? Our next batch of the comprehensive Machine Learning course is starting soon. Join a community of like-minded individuals and learn from industry experts.
29 December
4:30 pm to 6 pm
Enroll
*Limited seats
Want to know other batch availability?
FAQ's of Machine Learning Course
Felix-ITs tries and keeps the training with the most in-depth and comprehensive Machine Learning training in Pune that is in line with the industry requirements. With our training, you will learn the Machine Learning skills to help the world of Artificial Intelligence to successfully integrate, communicate, collaborate and automate processes.
Felix-ITs offers active placement assistance to all our learners who successfully complete the training. We have various tie-ups with over 50 top MNCs around the world. This way you can be placed in renowned firms looking for skilled professionals in Machine Learning.
The machine learning courses in Pune are quite reasonable and affordable. Felix-ITs also provides flexible payment options and loan assistance.
Anybody, irrespective of their educational or work background, can enroll in our Machine learning classes in Pune. However, knowledge about programming, calculus, probability, etc., is required
Yes, you can get the necessary training and certifications for AI and Machine learning from Felix-ITs. This can help you gain better knowledge and a better job in the future.
Anybody who wants to gain better theoretical and practical knowledge about machine learning and artificial intelligence can enroll themselves in Felix-ITs’ Machine Learning course in Pune.
Skills like applied mathematics, computer science, NLP, neural networks, etc., are required to learn Machine Learning.
Machine learning is a challenging course, but the Machine learning training in Pune provides a comprehensive syllabus and personalized mentorship to make it easier for the students.
Anybody who is willing to learn machine learning or Artificial intelligence can join Machine Learning classes in Pune by Felix-ITs.
A beginner can learn machine learning by setting their goals, picking a tool, and practicing on databases.
As Felix-ITs provides theoretical knowledge with a comprehensive syllabus, mentorship, personalized feedback, and practical training, it is considered to be one of the best Machine learning training institutes in Pune.
Felix-ITs’ machine learning course in Pune fees is quite nominal and affordable.
Machine Learning Basics
1. How do you select the right algorithm for a Machine Learning task?
Selecting the right algorithm for a Machine Learning task involves considering the problem's nature and data. Begin by defining your problem (classification, regression, clustering, etc.), then assess your dataset's size, quality, and characteristics. Choose a simple algorithm like linear regression for basic tasks with structured data. For more complex tasks, explore decision trees, random forests, or gradient boosting.
If dealing with unstructured data like text or images, consider deep learning with neural networks. Experiment with multiple algorithms, cross-validation, and performance metrics to determine the best fit. Ultimately, the choice depends on the specific task, data, and the trade-off between accuracy, interpretability, and computational resources. A machine learning basic course can help gain a proper understanding of these topics.
If dealing with unstructured data like text or images, consider deep learning with neural networks. Experiment with multiple algorithms, cross-validation, and performance metrics to determine the best fit. Ultimately, the choice depends on the specific task, data, and the trade-off between accuracy, interpretability, and computational resources. A machine learning basic course can help gain a proper understanding of these topics.
2. What is overfitting, and how can it be prevented or mitigated?
Overfitting is a common problem in machine learning where a model becomes overly specialized in the training data, resulting in poor performance on unseen data. This happens when the model captures noise and outliers, rather than generalizing the underlying patterns. To prevent or mitigate overfitting, several strategies can be employed. Cross-validation helps assess a model's performance on various data subsets, revealing potential overfitting. Regularization techniques like L1 or L2 regularization introduce penalties on complex model parameters, discouraging them from fitting the training data too closely.
3. What is the bias and variance trade-off in Machine Learning?
The bias-variance trade-off in machine learning is a pivotal consideration in model development. It revolves around achieving a delicate equilibrium between two crucial aspects. On one hand, we strive to minimize bias, which corresponds to the model's ability to capture intricate patterns in the training data. Reducing bias allows the model to fit the training data more closely. On the other hand, we aim to minimize variance, which pertains to the model's sensitivity to noise in the data, enabling it to generalize well to unseen examples. Striking the right balance is essential since an excessively biased model underfits and is oversimplified, while a high-variance model overfits, capturing noise instead of genuine patterns.
4. What is feature engineering, and why is it important in Machine Learning?
Feature engineering is the process of carefully selecting, manipulating, or generating new data attributes from raw data to enhance a machine learning model's performance. It is crucial in machine learning basic because the quality of features directly influences a model's ability to learn and make accurate predictions.
Effective feature engineering can uncover hidden patterns, reduce noise, and increase the model's efficiency, ultimately enhancing its predictive power. It's about customizing data to gain valuable insights and enhancing the performance and generalization of machine learning models.
Effective feature engineering can uncover hidden patterns, reduce noise, and increase the model's efficiency, ultimately enhancing its predictive power. It's about customizing data to gain valuable insights and enhancing the performance and generalization of machine learning models.
5. What is cross-validation, and why is it used?
Cross-validation is a vital statistical method used in machine learning basic and data analysis. It involves dividing a dataset into distinct subsets, typically a training set and a testing set.
The model is trained on one subset and evaluated on another, repeating this process multiple times. Cross-validation helps in assessing a model's performance, revealing its generalization capabilities, and detecting potential overfitting issues. Allows a more robust and accurate estimate of how well a model will perform on new, unseen data.
The model is trained on one subset and evaluated on another, repeating this process multiple times. Cross-validation helps in assessing a model's performance, revealing its generalization capabilities, and detecting potential overfitting issues. Allows a more robust and accurate estimate of how well a model will perform on new, unseen data.
6. What are hyperparameters in Machine Learning basic, and how are they tuned?
Hyperparameters in machine learning are critical settings that influence a model's performance. These parameters, such as learning rates, tree depths, and regularization strengths, are not learned from data but are chosen by the data scientist.
Hyperparameter tuning is the process of optimizing these settings to achieve the best model performance. This is typically achieved through systematic experimentation using methods like grid search, testing predefined hyperparameter values, or random search, exploring hyperparameter combinations randomly. The goal is to find the ideal hyperparameter values that maximize a model's accuracy and generalization.
Hyperparameter tuning is the process of optimizing these settings to achieve the best model performance. This is typically achieved through systematic experimentation using methods like grid search, testing predefined hyperparameter values, or random search, exploring hyperparameter combinations randomly. The goal is to find the ideal hyperparameter values that maximize a model's accuracy and generalization.
7. What is the difference between classification and regression in Machine Learning?
Classification and regression are two fundamental tasks in Machine Learning basic. Classification involves sorting data into predefined categories or labels, making it suitable for tasks like image recognition or spam detection. In contrast, regression aims to predict numeric values, making it ideal for tasks like predicting house prices or stock market trends. Both supervised learning methods have specific roles: classification for discrete outcomes, like sorting images into categories, and regression for continuous predictions, such as forecasting house prices. They are tailored for different real-world tasks, ensuring accurate results.
8. How does deep learning differ from traditional Machine Learning algorithms?
Deep learning is a type of machine learning, but it's special because it uses deep networks with many layers. These layers help it learn complex things from data. It's great for tasks like recognizing images or understanding speech. But deep learning needs lots of data and strong computers to work well. Traditional machine learning is not as deep and doesn't handle complex tasks as easily as deep learning.
9. What is reinforcement learning and what are some practical applications?
Reinforcement learning is a way for computers to learn by trial and error, like how we learn to ride a bike. In this method, a computer agent figures out the best actions to take in a given situation to get the most rewards. It's used in lots of useful stuff, like making robots smart, helping self-driving cars navigate safely, or even teaching computers to play games at superhuman levels, like AlphaGo did with Go. It's also behind those helpful recommendation systems that suggest movies, books, or products we might like. So, reinforcement learning is like computer learning from its mistakes to do tasks better.
10. What are some common evaluation metrics for Machine Learning models?
Evaluation metrics for Machine Learning models vary based on the problem at hand. In classification tasks, common metrics encompass accuracy, precision, recall, and the F1-score. For regression, we often use mean squared error (MSE), mean absolute error (MAE), and R-squared. These metrics help gauge the performance of models, providing a clear understanding of their accuracy, ability to classify correctly, and predictive power in regression scenarios. The choice of metric depends on the specific goals and requirements of the machine learning project.
Learning a lot within a course period while working on individual project. Placement assistance is also very much appreciated. I would like to highly recommended to everyone. Specially thanks to my mentor Tanmay sir. He is very supportive and always motivate to every student. The teaching staff has a good experience in the field of UI/UX design. Good Institute to start your UI/UX journey. I also appreciate to recruitment team who always share the openings.