Machine Learning Certification Course

Learn to Train Your First Neural Network in Python In Weeks

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Rated 4.9/5 from over 600 reviews.

Become a Better
ML Engineer

Over 200 students have already 
registered for the next batch!

When do you want to start?
  • Construct counterfeit neural organizations with Tensorflow and Keras.
  • Arrange pictures, information, and opinions utilizing profound learning.
  • Make forecasts utilizing direct relapse, polynomial relapse, and multivariate relapse.
  • Information Visualization with MatPlotLib and Seaborn.
  • Actualize ML at gigantic scope with Apache Spark's MLLib.
  • Comprehend support learning - and how to construct a Pac-Man bot.
  • Arrange information utilizing K-Means grouping, Support Vector Machines (SVM), KNN, Decision Trees, NMLve Bayes, and PCA.
  • Use trMLn/test and K-Fold cross approval to pick and tune your models.
  • Fabricate a film recommender framework utilizing thing based and client-based shared separating.
  • Clean your information to eliminate exceptions.
  • Plan and assess A/B tests utilizing T-Tests and P-Values.
  • You'll require a personal computer (Windows, Mac, or Linux) equipped for running Anaconda 3 or more current. The course will walk you through introducing the essential free programming.
  • Some earlier coding or scripting experience is required.
  • At any rate secondary school level, numerical abilities will be required.

ML is the study of getting PCs to act without being expressly modified. In the previous decade, ML has given us self-driving vehicles, commonsense discourse acknowledgment, compelling web search, and an incomprehensibly improved comprehension of the human genome.

  • Programming designers or software engineers who need to change into the worthwhile information science and ML profession way will gain so much from this course.
  • Information examiners in the account or other non-tech enterprises who need to change into the tech business can utilize this course to figure out how to dissect information utilizing code rather than instruments. Yet, you'll need some related knowledge in coding or scripting to be effective.
  • On the off chance that you have no earlier coding or scripting experience, you ought NOT to take this course - yet. Go take an early on Python course first.

Why Study Machine Learning?

Jobs on the Rise.

According to a recent survey, jobs in machine learning are rising.

Lucrative Career.

Machine learning is a constantly evolving and lucrative career.

High Paying Jobs.

This is a growing sector with many high-paying jobs.

Fast Growing Field.

The field is growing quickly, making it an exciting and rewarding career path.

Machine Learning Course Syllabus

In the past decade, we have seen a dramatic increase in the use of machine learning. This technology is being used across a variety of industries, from retail to finance, and its impact is only increasing. For businesses, machine learning can be used to improve customer service, target marketing, and even predict future trends. For individuals, machine learning is providing new opportunities for personalization and customization. In this session, we will provide an introduction to machine learning, including its definition, history, and applications.

Supervised learning is a type of machine learning algorithm that uses a known set of training data to train a model to produce desired output. Linear regression is a type of supervised learning algorithm that finds the linear relationship between a dependent variable and one or more independent variables.

In this session we will discuss more about Supervised Learning and Linear Regression.

There are many supervised machine learning models that can be used for classification, but logistic regression is a popular choice due to its simplicity and interpretability. In this session, we'll explore how logistic regression works and how it can be used for classification tasks. We'll also see how to train and evaluate a logistic regression model using Python.

Decision trees are a powerful tool for both classification and regression tasks. A decision tree is a model that is used to predict the value of a target variable by learning simple decision rules inferred from the data features. Random forests are a modification of decision trees. In this session, we will discuss more about Decision Tree and Random Forest.

In machine learning, there are a few different methods for classification. Naive Bayes and Support Vector Machine are two of the most popular methods.

In this session, we'll take a look at both methods and see how they compare.

Looking for the complete module?
Download Full Syllabus

Certificates you will receive post completion of Machine Learning Course

  1. We acknowledge your hard work and dedication towards the program by bestowing a course completion certificate from YHills.
  2. We also provide internship certificates from YHills and top-hole universities after post successful completion of the internship.
  3. We purvey an Industry certificate from renowned and prestigious companies as an incentive.

Machine Learning Course Fee

Quality learning, simplified and budget-friendly – just for you!

“The YHills course is straightforward, featuring hands-on projects and a real-world approach, I recommend it!”

Priyanshu K

5-star rating

Mentor-Led Program
6000 /Course

You’ll Get Access to:

Professional Program
9000 /Course
Everything in Mentor-Led, Plus:

Meet Our Mentors

We are ready to assist, advise, encourage and listen as you begin your professional courses at YHills.

Garima Mittal

AutoCAD Trainer 

Indrajeet Kumar

Full Stack Developer

Uttam Grade

Data Scientist

Vaishnavu C V

Cyber Security Consultant

T. Raja Stephenson

Digital Marketing Expert

Hear what our students says

40,000+ Students have already learned with YHills!

After taking this machine learning course, I have a much better understanding of how to use machine learning and data science in my business.

Anzu Thakur ML Engineer

I have to say, this course is one of the best I've ever taken. The lectures are very engaging and entertaining, which makes it easier to pay attention and remember.

Shubham Verma Software Programmer

I’ve never had this much fun learning a new skill. The instructor is super clear and helpful, and the lessons are well-paced. I always look forward to my next lesson!

Ravi Sharma Student

I've never been good at math, but I just wanted to share that this course was really helpful for me. The instructor made it easy to understand and I learned so much!

Sukriti Sharma Student

Mastering Machine Learning: A [Step-by-Step] Guide for Beginners

Have you ever noticed how the internet seems to know what you like?

Well, it’s because of something called machine learning. 

It’s like a smart computer program that learns from data and helps make decisions.

Machine learning is used in many things we see online, like the posts we see on social media and the ads that pop up.

It’s pretty cool.

Here’s a fun fact: Companies all over the world use machine learning, and it’s predicted that by 2030, the global machine learning market will be worth a lot of money—USD 419.94 billion, to be exact.

That’s a huge amount of money!

Read More Here


At YHills Edutech, we believe in providing excellent customer service, ensuring that all your queries are answered promptly and efficiently.

Yes, of course it is! Machine learning is a branch of artificial intelligence (AI) that focuses on the development of computer systems that can learn from data and make predictions based on new information.
Machine learning can be used in almost every industry! It has already been used to improve the quality of healthcare, solve crimes more efficiently than humans ever could before, predict stock prices accurately enough to make billions of dollars in profits, etc. The possibilities are endless!
Yes! You don't need any prior experience with math or statistics to take this course—we'll be using Python and R, which are both easy-to-learn languages that allow you to get your hands dirty with data right away.
Because machine learning is all about handling big data! The more data you have and the bigger your company is, the more machine learning will help you. It can help automate processes like predictive analytics or customer service chatbots. It can also help with image recognition or speech-to-text conversion. If you're working at a large company (or any company with lots of data), chances are there's a place for machine learning in your workflow somewhere.
A few examples include chatbots, self-driving cars, and autonomous weapons. Chatbots use machine learning to understand what you're saying and respond appropriately. Self-driving cars use machine learning to predict the actions of other drivers and pedestrians around them and then take action accordingly. And autonomous weapons use machine learning in order to learn about their environment and decide how best to attack targets based on what they see.

Learn Python in Weeks and Train Your First Neural Network.

Are you prepared for getting started on your journey in Machine Learning?