3 Month Course
12 hours per Week

Curriculum designed by Brillica Services and approved by E&ICT Academy IIT Guwahati

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about course

In the course, you will learn the concept of Machine Learning course Using Python will teach you the fundamentals of machine learning and explain how the Python programming language works with it. The goal of the machine learning with Python course is to provide top-notch instruction on statistical modeling, regression, and clustering methods. Our Machine learning course is certified by E&ICT Academy IIT Guwahati in which students will also gain a thorough understanding of the interoperability between Python and machine learning and the amazing applications it can provide. Through numerous projects included in the machine learning using Python and certification course, Brillica services, a well-known leader in the field, gives you the chance to turn your theoretical knowledge of machine learning and Python into a highly valued and job-oriented practical skill through our project-based training. Our Machine learning course with E&ICT Academy IIT Guwahati

Program hightlight

Instructor-led Classroom and Online Training modes

Designed for Working Professionals & Freshers

Work on live projects

Beginner to expert-Level training

Machine learing with python

Artificial Intelligence & deep learning concepts

Course Details


  • History of Python, features, current industry standards.
  • Basic syntax, data types
  • Control flow statements like — if, else if, if else.
  • Loops — For, While, nested loops
  • Control statements — continue, break, pass Creating Lists, Tuples, Dictionaries Inbuilt functions for Lists, Tuples, Dictionaries.
  • Creating your own functions.
  • Printing star patterns using functions.
  • Creating classes in Python. Accessing class variables, functions using objects.
  • Inheritance in Python.
  • Self, super keyword in python.
  • Creating modules.
  • Excepñon Handling
  • Database connect1vity

Machine Learning

  • Formulate a Machine Learning Problem
  • Problem Formulation
  • Framing a Machine Learning Problem
  • Differences Between Traditional Programming and Machine Learning
  • Differences Between Supervised and Unsupervised Learning
  • Randomness in Machine Learning Random Number Generation Machine Learning Outcomes
  • Machine Learning Datasets Structure of Data
  • Terms Describing Portions of Data
  • Data Quality Issues
  • Data Sources
  • Open Datasets
  • Examining the Structure of a Machine Learning Dataset Extract, Transform, and Load (ETL)
  • Loading the Dataset
  • Use Visualizations to Analyse Data
  • Visualizations - Histogram
  • Analysing a Dataset Using Box Plot Visualizations
  • Scatterplot
  • Heat Maps
  • Guidelines for Using Visualizations to Analyse Data
  • Prepare Data
  • Data Preparation
  • Data Types
  • Operations You Can Perform on Different Types of Data
  • Continuous vs. Discrete Variables
  • Data Encoding
  • Dimensionality Reduction
  • Impute Missing Values
  • Duplicates
  • Normalization and Standardization
  • Guidelines for Preparing Training and Testing Data
  • Splitting the Training and Testing Datasets and Labels
  • Setting Up and Training a Model
  • Hypothesis Confidence Interval
  • Machine Learning Algorithms
  • Algoriihm Selection
  • Guidelines for Setting Up a Machine Learning Model
  • Setting Up a Machine Learning Model
  • Train the Model
  • Iterative Tuning Bias Compromises
  • Model Generalization
  • Cross-Validation
  • k-Fold Cross-Validation
  • Dealing with Outliers
  • Feature Transformation
  • Transformation Functions
  • Scaling and Normalizing Features
  • The Bias—Variance Trade-off
  • Parameters
  • Regularization
  • Models in Combination
  • Guidelines for Training and Tuning the Model
  • Building Linear Regression Models
  • Linear Regression
  • Linear Equation
  • Linear Equation Data Example Straight Line Fit to Example Data
  • Linear Regression in Machine Learning
  • Linear Regression in Machine Learning Example Matrices in Linear Regression
  • Linear Model with Multiple Parameters Cost Function
  • Mean Squared Error (MSE) Mean Absolute Error (MAE) Coefficient of Determination
  • Build a Regularized Regression Model
  • Regularization Techniques
  • Overfitting and Underfitting Recurrent Neural Network Ridge Regression
  • Lasso Regression
  • Guidelines for Building a Regularized Linear Regression Model
  • Building a Regularized Linear Regression Model
  • Build an Iterative Linear Regression Model
  • Gradient Descent
  • Global Minimum vs. Local Minima
  • Learning Rate
  • Gradient Descent Techniques
  • Building an Iterative Linear Regression Model
  • Building Classification Models
  • Building Classification Models Train Binary Classification Models Linear Regression
  • Shortcomings Logistic Regression
  • Decision Boundary
  • Cost Function for Logistic Regression A Simpler Alternative for Classification
  • K-Nearest Neighbour (k-NN)
  • Guidelines for Training Binary Classification Model
  • Train Multi-Class Classification Models
  • Multi-Class Classification Multinomial Logistic Regression
  • Guidelines for Training Multi-Class Classification Models Training a Multi-Class
  • Classification Model
  • Evaluate Classification Models
  • Model Performance
  • Confusion Matrix
  • Classifier Performance Measurement
  • Accuracy
  • Precision
  • Recall
  • Precision— Recall Trade-off F1 Score
  • Guidelines for Evaluating Classification Models Evaluating a Classification Model
  • Tune Classification Models
  • Hyperparameter Optimization
  • Grid Search Randomized Search
  • Guidelines for Tuning Classification Models
  • Tuning a Classification Model
  • Building Clustering Models
  • Build K Means Clustering Models
  • K Means Clustering K Determination Elbow Point
  • Cluster Sum of Squares
  • Guidelines for Building a K Means Clustering Model
  • Build Hierarchical Clustering Models
  • K Means Clustering Shortcomings
  • Hierarchical Clustering
  • Hierarchical Clustering Applied to a Spiral Dataset
  • Dendrogram
  • Building a Hierarchical Clustering Model
  • Build Decision Tree Models
  • Decision Tree
  • Classification and Regression Tree (CART)
  • Gini Index/Entropy CART Hyperparameters Pruning
  • One Hot Encoding
  • Decision Tree Algorithm Comparison Decision Trees Compared to Other Algorithms
  • Guidelines for Building a Decision Tree Model Building a Decision Tree Model
  • Build Random Forest Models
  • Ensemble Learning Random Forest
  • Random Forest Hyperparameters Feature Selection Benefits
  • Guidelines for Building a Random Forest Model Building a Random Forest Model

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Who Can Apply ?
Job Roles
Business Intelligence Analyst

Analyses linked products, markets, or trends to evaluate market strategies. uses technologies and data from business intelligence to find and keep an eye on both existing and potential clients. analyses technological trends to find markets for new product creation and strategies to increase sales of already existing products.

Machine Learning Engineer

An IT professional that specializes in developing and creating self-contained artificial intelligence systems to automate prediction models is known as a machine learning engineer. Machine learning engineer develops the algorithms that are eventually used in artificial intelligence.

Data Scientist

A data scientist is a specialist in data who concludes sizable data sets to assist organizations in resolving challenging issues. To do this, data scientists combine a deep knowledge of their business and sector with computer science, maths, statistics, and modeling to uncover new opportunities and tactics.

AI Developer

The tools and applications for artificial intelligence that a certain company might use are created by an AI developer. They design systems that can adapt to the needs of the company based on the data they gather and analyze.

NLP Scientist

The interface between common human language and a computer's capacity to process and analyze natural language data is the responsibility of NLP engineers. The field of natural language processing (NLP) involves a combination of computer science, information sciences, artificial intelligence (AI), and linguistics.

Algorithm Engineer

A professional who creates, develops, and implements algorithms to address challenging computational issues is known as an algorithm engineer or developer. Their main duty is to design dependable, scalable, and effective algorithms that can handle massive amounts of data while producing precise results.

Tools To be Covered
Meet the Mentor

Vikas Singh

Data Science & Full Stack web development Trainer An Industry Experienced Data science & Machine Learning Expert having a working knowledge of all Analytics Tools & Technologies. He has trained over 400 Candidates in various Data Analytics Tools.

Student testimonials

Ronak Dua

Data science

Undoubtedly you provide the best Data Science course, I was already impressed in the demo classes and the trainers are so experienced and helping that you will never feel confused and left out, currently i am placed at ML Technologies all because of Brillica services, thanx A ton!

Aayush Rawat

data science

My overall experience at Brillica was up to the mark. The trainers/teachers are well versed with the topics, and are quite supportive and helpful. Anyone who wants to brush up their basics or enhance their skills should join here, they provide the best Data Science course in Dehradun & Uttarakhand.

Suman Pandey

Data science

I joined Brillica to take the training of Data Science I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. They also provide the recording which is excellent for revising. The courses in Brillica services are so informative and delivered in a way that is clear and concise.

Srishti Singhal

data science

Really nice experience. I was enrolled in Data Science program and liked the way teachers concentrate on every child even in online classes. Great faculty who supports you even after completion of course. You will not regret investing your money and time here. Just do all the assignments told and you will be good to go. Thanks.

Tech World 2021

data science

I did IOT and Data Science from here . Very nice coaching is really helping me in my carrer . Class mai hasi mazak bhi hota hai . I would highly recommend this place for IOT and Data Science . Enroll today.

Shivam Amoli

data science

Opted for Data science using Python, and it was a great start. All concept were explained by Gurjas sir in every possible way. All doubts cleared. Had a very good experience learning.

Anmol Jaiswal

data science

It was best 6 months spent here to learn data Science skills.

Naincy Prabha

data science

I am from uttranchal University, Uttrakhand.I had a great experience in learning here.
Our Alumni Work At
Career Services
Brillica Services provides 100% placement assistance to the students and provide them assistance throughout to way so that they can go for the career they want. After the completion of 50% of the course we start preparing students for the interviews, also we provide the information about the latest hirings so that they can apply for it.
We provide interview preparation sessions for the students and provide them knowledge about how to appear in the interviews and help them in improving their communication plus we provide them the insight of how to crack an interview.
Creating a professional profile is very necessary if you want to appear for an interview, we help students in making a professional profile and add the skills they require to add in their resumes.

Brillica Services provides the Machine learning certification from E&ICT Academy IIT Guwahati which is one of the best certifications that you can get right now. Machine learning course with E&ICT Academy IIT Guwahati certification will add skills that will bring weight to your resume and make you superior amongst your peers with no doubt.

Projects Delivered

Covid Analysis

Sentimental Analysis

Sales Prediction

Recommendation System

Loan eligibility prediction

Inventory Demand forecasting

Related Course
Python machine learning artificial intelligence
Latest blogs

1. What is a Machine learning course?

In a Machine learning course, you get to learn the development of algorithms and software that can make predictions based on the given data.

2.Is Machine learning very difficult?

Machine learning is not difficult although if you are from a programming background then it might be easier for you than you would have thought otherwise with our curriculum and experienced trainers you won’t find Machine learning courses difficult.

3. Is Python enough for Machine learning?

Yes, Python is a commonly used language for machine learning because of its simplicity, moreover, Python language has recently been updated and the few drawbacks it has are now been removed as well so Python is enough for Machine learning.

4. Which is better Artificial Intelligence or Machine learning?

AI is a part of Machine learning because we use Machine learning algorithms to develop AI capabilities.

5. Who is eligible for the Machine Learning course?

There are no eligibility criteria for a Machine learning course anyone can do it if they are interested and want to make a career in Machine learning.

6.Can I get a job with Machine learning?

Yes, Machine learning is a vast field and is currently in demand so actually, there is a shortage of skilled people in Machine learning so if you acquire this skill, you not only get a job but there are chances that you will get a high-paying job as well.

7. How can I get a Machine learning certification from E&ICT Academy IIT Guwahati?

If you enroll in our course, we provide a Machine learning course and provide certification from E&ICT Academy IIT Guwahati as we have a collaboration.

8. What is E&ICT Academy IIT Guwahati Machine Learning certification?

E&ICT or Electronics & ICT Academy provides training programs that focus on learning from basic to advance level topics in the latest and in-demand technologies, Machine Learning course with E&ICT Academy IIT Guwahati certification is a certification that you will get after the completion of the course.