Essentials of Machine Learning and Artificial Intelligence - Get SDE Ready

Home Course Essentials of Machine Learning and Artificial Intelligence

Essentials of Machine Learning and Artificial Intelligence

Welcome to the Essentials of Machine Learning and Artificial Intelligence course, your gateway to mastering two of the most transformative technologies shaping the future. Whether you’re an aspiring data scientist, software developer, or AI enthusiast, this course is meticulously crafted to guide you from the foundational principles to advanced applications of machine learning and artificial intelligence.

Enrolled:11

Last Updated:22/03/2025

22 LPA

Average Package

120%

Salary Hike

83 LPA

Highest Package

What I will learn?

Master the Fundamentals of AI and Machine Learning: Gain a deep understanding of supervised, unsupervised, and reinforcement learning with practical applications.
Harness Neural Networks and Deep Learning: Explore advanced techniques to process complex data and develop intelligent AI systems.
Gain Hands-On Experience With Industry Projects: Develop practical skills through projects like recommendation systems, predictive analytics, and image recognition.

Build and Optimize Machine Learning Models: Learn to design, train, and fine-tune models to tackle real-world challenges effectively.
Dive Into Natural Language Processing (NLP): Create chatbots, text analysis tools, and language translation models for diverse use cases.
Explore AI in Emerging Technologies: Understand how AI integrates with IoT, robotics, and edge computing to shape the future.

Material Includes

65+ Live Classes & Recordings

24/7 Live Doubt Support

22+ Hands-on Live Projects

Topic Wise Quizzes

Case Studies

Access to Global Peer Community

Lifetime Support

Referrals

About Course

Welcome to the Essentials of Machine Learning and Artificial Intelligence course, your gateway to mastering two of the most transformative technologies shaping the future. Whether you're an aspiring data scientist, software developer, or AI enthusiast, this course is meticulously crafted to guide you from the foundational principles to advanced applications of machine learning and artificial intelligence.

Course Curriculum

Introduction to the Course
A brief introduction to what's coming ahead, and how you can make the most out of it. In this module, we answer some very basic yet crucial questions to lay the foundations of the journey we are going to begin. Excited to have you onboard!!!

What is Machine Learning?

00:00
How exactly does Machine Learning work?

00:00
Busting Myths about Machine Learning and Artificial Intelligence

00:00
Understanding the Relationship Between Data Science, Machine Learning, and AI

00:00
Navigating your Journey: What to Expect from this Course

00:00

The Python Programming Language
Before starting with ML, let's first have a look at the programming language we'll be using. In this module, we'll dive into Python programming essentials tailored for Machine Learning. Expect to cover basic syntax, essential data structures, functions and modules. By the end, you'll be equipped with the foundational skills necessary to start programming in Python.

Introduction to Python

00:00
Basics of Python: Arithmetic and Variables

00:00
Built-In Data Structures in Python: Integers and Booleans

00:00
Built-In Data Structures in Python: Lists and Tuples

00:00
Built-In Data Structures in Python: Strings and Dictionaries

00:00
Python Quiz 1
Functions in Python

00:00
Conditional Statements and Loops in Python

00:00
Object-Oriented Programming (OOP) Concepts

00:00
Iterators in Python

00:00
File and Error Handling in Python

00:00
Asynchronous Programming in Python

00:00

Introduction to Git and Github
In the world of software development, version control is essential for managing changes to source code over time. Git and GitHub are powerful tools that facilitate version control and collaboration among developers. This module introduces you to the basics of Git and GitHub, highlighting their significance and functionality.

Data Acquisition
Data acquisition is a critical step in the data science pipeline. It involves gathering data from various sources to use in analysis, model training, and decision-making processes. The quality and quantity of data collected significantly impact the insights and outcomes of any data science project. This module will cover the key aspects of data acquisition, including types of data sources, methods of data collection, and best practices to ensure data quality and relevance.

Web Automation using Selenium
Selenium is a powerful tool commonly used for web automation, allowing developers to interact with web pages programmatically. In this module we'll be discussing Selenium for web automation, covering its features, benefits, and common use cases.

Databases
Data is the new fuel of this world but data is unorganized information, so to organize that data we make a database. A database is the organized collection of structured data usually controlled by a database management system (DBMS). Databases help us easily store, access, and manipulate data held on a computer.

Getting Started with Machine Learning
Machine Learning (ML) is a branch of artificial intelligence (AI) concerned with the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. ML algorithms are designed to learn patterns and relationships from data, allowing them to make predictions or decisions based on new or unseen data. The core objective of machine learning is to develop systems that can automatically improve their performance over time as they are exposed to more data, thereby enabling them to adapt to new situations and make informed decisions.

Numpy

Linear Algebra for ML
Linear algebra is essential for understanding and creating machine learning algorithms, especially neural networks and deep learning models. It is the mathematical foundation that solves the problem of representing data as well as computations in machine learning models. Linear algebra enables the representation of data points as vectors and allows for efficient computation of operations on these vectors. Linear algebra is used in machine learning algorithms such as neural networks, support vector machines, image processing, and principal component analysis

Pandas
Pandas is a widely used Python library for data manipulation and analysis. It provides high-level data structures and functions designed to make working with structured data fast, easy, and expressive. This note serves as an introduction to Pandas, covering its key features, data structures, and common operations.

Data Visualisation
Data visualization is the graphical representation of data and information. It plays a crucial role in machine learning (ML) and data science by aiding in the understanding, interpretation, and communication of complex datasets. The primary objectives of data visualization in these fields are to explore, analyze, and present insights from data to facilitate informed decision-making.

Probability Distribution and Statistics
Probability distribution and statistics are fundamental concepts in the field of mathematics and are extensively used in various disciplines, including but not limited to, economics, engineering, social sciences, and natural sciences.

Linear Regression
Linear regression is a fundamental statistical method used in machine learning to model the relationship between a dependent variable and one or more independent variables. This technique aims to find the best-fit line that minimizes the difference between the observed data points and the predicted values. Linear regression is widely used for predictive analysis, trend forecasting, and determining the strength of predictors. In this lesson, we will explore the basics of linear regression, including its assumptions, the least squares method for fitting a model, and evaluating model performance using key metrics. Understanding linear regression is essential as it forms the basis for more complex regression techniques and serves as a foundational tool in data analysis and machine learning.

Scikit – Learn
This module introduces you to Scikit Learn - one of the most important and widely used libraries by data scientists all over the world. Scikit-Learn, also known as sklearn, is an open-source machine learning library for the Python programming language. It provides simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and matplotlib.

Optimisation Algorithms

Locally Weighted Scatterplot Smoothing

Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a widely used statistical method for estimating the parameters of a statistical model. It is based on the principle of maximizing the likelihood function, which measures the probability of observing the given data under the assumed model.

Logistic Regression
Logistic regression is a supervised machine learning algorithm used for classification tasks where the goal is to predict the probability that an instance belongs to a given class or not. Logistic regression is a statistical algorithm which analyzes the relationship between two data factors. The module explores the fundamentals of logistic regression, its types and implementations.

Classification Measures
Classification is a fundamental task in machine learning (ML), involving the categorization of data into predefined classes or categories based on input features. Accurately evaluating the performance of classification models is essential for assessing their effectiveness in real world applications. This module provides a detailed examination of various classification measures used in ML.

Data Preprocessing
Data preprocessing is an important step in the data mining process. It refers to the cleaning, transforming, and integrating of data in order to make it ready for analysis. The goal of data preprocessing is to improve the quality of the data and to make it more suitable for the specific data mining task.

Principal Component Analysis (PCA)
As the dimensionality of a dataset increases, the requisite volume of data needed to achieve statistically significant results escalates exponentially, giving rise to challenges such as overfitting, prolonged computational time, and diminished accuracy of machine learning models. This phenomenon, known as the curse of dimensionality, poses significant issues when dealing with high-dimensional data.

Project 1: Implementing Regression Techniques

Decision Trees and Random Forests
Decision trees are intuitive and powerful tools for both classification and regression tasks in machine learning. They work by splitting the data into subsets based on feature values, creating a tree-like model of decisions. Each node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome. This method is highly interpretable and easy to visualize, making it popular for understanding complex data. Random forests, on the other hand, are an ensemble learning method that enhances the predictive performance of decision trees. By constructing a multitude of decision trees during training and outputting the mode or mean prediction of the individual trees, random forests reduce the risk of overfitting and improve accuracy. This technique leverages the power of multiple trees to achieve robust and reliable predictions. In this module, we will explore the construction, advantages, and practical applications of both decision trees and random forests, highlighting their role in solving real-world machine learning problems.

K-Nearest Neighbors (KNN)
K-Nearest Neighbors is a simple yet powerful supervised machine learning algorithm used for classification and regression tasks. It's considered a non-parametric and instance-based learning algorithm, meaning it makes predictions based on the similarity of input data points.

Support Vector Machines
Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression, and even outlier detection tasks. SVMs can be used for a variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection, and anomaly detection. SVMs are adaptable and efficient in a variety of applications because they can manage high-dimensional data and nonlinear relationships.

Clustering Fundamentals
Clustering in machine learning is a form of unsupervised learning where the goal is to partition a dataset into groups, or clusters, based on the similarity of data points within each group.

Natural Language Processing
Natural language processing (NLP) is a field of computer science and a subfield of artificial intelligence that aims to make computers understand human language. You may have used some of these applications yourself, such as voice-operated GPS systems, digital assistants, speech-to-text software, and customer service bots. Now, it's time to see how these applications work behind the scenes.

Introduction to NLP

00:00
NLP Techniques

00:00
Working of NLP

00:00
Working of NLP – Data Acquisition

00:00
Working of NLP – Text Cleaning

00:00
Working of NLP – Text Preprocessing

00:00
Working of NLP – Feature Engineering

00:00
Working of NLP – Model Building

00:00
Working of NLP – Evaluation and Deployment

00:00
Future Scopes

00:00

Naive Bayes Classifier
Naive Bayes is a family of simple yet powerful probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Despite its simplicity, Naive Bayes performs remarkably well in many real-world situations and is particularly popular for text classification problems like spam detection, sentiment analysis, and more.

Bonus Module 1 – Gradient Boost Models and more
In this module, we take a look at gradient boosting models and fine-tuning model hyperparameters using Optuna, GridSearch CV and RandomSearchCV.

Introduction to Deep Learning
Deep learning is a subset of machine learning based on artificial neural networks, inspired by the structure and function of the brain. It focuses on training models, known as deep neural networks, which consist of multiple layers of interconnected nodes or neurons, to automatically learn hierarchical representations of data.

Neural Networks
Neural Networks are computational models that mimic the complex functions of the human brain. The neural networks consist of interconnected nodes or neurons that process and learn from data, enabling tasks such as pattern recognition and decision making in machine learning. The article explores more about neural networks, their working, architecture and more.

TensorFlow

Keras

PyTorch

Introduction to Image Processing

Project 2: Facial Recognition System

Convolutional Neural Networks
A Convolutional Neural Network (CNN) is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks.

Training Data Loaders, Data Augmentation, and Google Colab
In machine learning, especially in computer vision tasks, preparing the data is as crucial as choosing the model architecture. This involves creating data loaders to efficiently handle and preprocess the data, applying data augmentation techniques to enhance the training dataset, and utilizing platforms like Google Colab to streamline the training process.

Transfer Learning
We, humans, are very good at applying the transfer of knowledge between tasks. This means that whenever we encounter a new problem or a task. Similarly Transfer learning is a smart method in machine learning where a model uses knowledge from one task to help with a different, but related, task.

Back to NLP: Markov Chains
Markov chains are a mathematical system that undergoes transitions from one state to another on a state space. They are a powerful tool for modeling sequential data and can be applied to various fields, including text generation. This module will explore how Markov chains can be used to generate text, providing both a theoretical foundation and practical implementation.

Recurrent Neural Networks
Recurrent Neural Network (RNN) is a type of Neural Network where the output from the previous step is fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other.

Long Short-Term Memory Networks
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to better capture long-term dependencies in sequential data. LSTMs address the vanishing gradient problem that plagues standard RNNs, making them more effective at learning patterns over extended sequences.

Gated Recurrent Units (GRUs)
Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture introduced to address the vanishing gradient problem inherent in standard RNNs. They are designed to capture long-term dependencies more efficiently than traditional RNNs while being computationally lighter and simpler than Long Short-Term Memory (LSTM) networks.

Word Embeddings
Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space.

Contextual Embeddings

Bonus Module 2 – The Art of Ensembling Models
Ensembling models refers to the technique of combining multiple machine learning models to improve overall performance. The idea is to leverage the strengths of different models to achieve better predictive accuracy and robustness

Reinforcement Learning
Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Generative Models

Introduction to LLMs

Student Ratings & Reviews

No Review Yet

No Review Yet

Our Instructors

Weekly schedule

Core Sessions

Live, hands-on, activity-led sessions conducted by the instructor

Sat, 12:00 PM – 3:00 PM (IST)

Sun, 12:00 PM – 3:00 PM (IST)

Doubt Sessions

Live, hands-on, activity-led sessions conducted by the instructor

Mon-Fri, 9:00 PM – 11:00 PM (IST)

What You Will Get Post Completion Of Your Program

Resume Building

Create a powerful, job-winning resume that highlights your skills and maximizes your chances of securing top tech roles.

Linkedin Profile Building

Optimize your LinkedIn profile to attract recruiters, showcase expertise, and unlock new career opportunities in tech.

Mock Interviews

Gain confidence with expert-led mock interviews, personalized feedback, and real-world interview scenarios.

Referrals

Enhance your job search with strong industry referrals, connecting you to exclusive opportunities at top tech companies.

Home Course Essentials of Machine Learning and Artificial Intelligence

Essentials of Machine Learning and Artificial Intelligence

Welcome to the Essentials of Machine Learning and Artificial Intelligence course, your gateway to mastering two of the most transformative technologies shaping the future. Whether you’re an aspiring data scientist, software developer, or AI enthusiast, this course is meticulously crafted to guide you from the foundational principles to advanced applications of machine learning and artificial intelligence.

Enrolled:11

Last Updated:22/03/2025

Enrollment validity: Lifetime

Total Enrolled

11
Duration

200 hours
Certificate

Certificate of completion

Lifetime Access
65+ Live Classes & Recordings
22+ Hands-on Live Projects
Topic Wise Quizzes
Interview Prep Material
Live Classes
Continuous Updates

We have helped build 1000+ Careers

Get Ready SDE has been a game-changer in my learning journey, providing high-quality and affordable courses in DSA and System Design. Unlike other expensive platforms, Get Ready SDE offered exceptional instruction, with Karthik simplifying complex topics like graphs and dynamic programming in a way that made them easy to grasp. The course material closely aligned with real interview scenarios, helping me improve my coding skills and confidently crack multiple interviews, leading to a significant salary hike.

Before joining Get SDE Ready, I struggled with DSA and felt overwhelmed by the competitive job market. The 9-month program in DSA, web development, and system design transformed my approach to problem-solving and coding. With expert guidance and structured learning, I gained the confidence and skills needed to excel. Thanks to this program, I secured a placement at Ecom Express, and now I feel fully prepared to take on challenges as a software engineer.

As a college student, I felt uncertain about placements and faced challenges with DSA due to the competitive job market. However, after joining Get SDE Ready’s 9-month program in DSA, web development, and system design, he significantly improved his skills and confidence. This program played a crucial role in helping him secure a placement at Ecom Express. Now, I feels highly confident in his abilities as a software engineer.

Get Sde Ready provided comprehensive interview preparation guidance, curriculum, live classes, resume building sessions to offer negotiation, mock interviews helped me get over my nervousness and crack interview at Paypal. I really love the curriculum and the way Arun teaches is really commendable.

I was trying to upskill myself from free resources on youtube. But I was not able to maintain consistency. So I joined Get SDE Ready whose proper structured curriculum has helped me be consistent and 24/7 live doubt support allowed me to clarify my doubts anytime I wanted.

I was stuck at Expedia at SDE-I position for quite some time. Also, I was not able to design well. With time, my problem solving skills were also going down because of no practise. I joined DSA & System Design Batch for 5 months that helped me grab a good promotion in my current company. I would highly recommend this course.

After struggling to get promoted, I joined Get SDE Ready’s course on DSA and System Design. The skills and knowledge I gained made all the difference, leading to my long-awaited promotion. Not just that, I was rated one of the highest performers in Amazon in that quarter.

I was working in Dunzo and was being layed off being of no funding in the company. I joined Get SDE Ready and their DSA & System Design course helped me grab placement in 4 months. I just made use of their 24*7 doubt support to clear out all my doubts and their mock interviews helped me gain a lot of confidence.

I was getting rejected in all the top product based companies but after joining Get SDE Ready, I was able to fix my resume and coding skills. That’s why cracked Salesforce after huge hustle. I liked the structured curriculum and mentorship provided by the mentors. I really like the way live classes are being conducted and on a regular basis.

I owe a great deal of my career success to Get SDE Ready! Their expert guidance not only enhanced my problem solving and design skills but also helped me crack 3 offers: Smartbear, CME and Light Fury Games with 60% hike. The placement team helped me with fixing the interviews and also guided me on how to negotiate.

I began my upskiling journey with Get SDE Ready after a lot of research and wasn’t disappointed. Before joining, I had also checked out profiles of all the instructors. I really liked the personal touch in live classes and the structured curriculum. I would highly recommend the course for interview preparation.

I had paid lakhs in one of the institutes and they didnt provide me classes. Then I found out GET SDE READY through insta ad. I was stuck in a service based company and didn’t know how to go to product based company. Get SDE Ready’s curriculum and live classes helped me shift to product based company.

I was a QA engineer in a service based company and I was really scared how I could shift my domain to sde. Get SDE Ready helped me shift to a product based company and also my domain. I was a beginner in coding. Their structured curriculum helped me learn coding.

I joined DSA & System Design Batch in January. The consistent efforts of Arun and Vinamer along with all the TAs in answering our doubts as and when we posted them gave me huge confidence. It helped me crack 2 offers. They also provided me proper guidance about how to negotiate my CTC. I would highly recommend the course to everyone.

I was a freelancer previously and wanted to build something of my own. I used to lack problem solving and design skills. After completing the DSA course with Get SDE Ready, it enabled me to start my own company and code on my own. Also, I have cracked 2 offers that have given me security that if my startup doesn’t work, I will join that.

I was very uncertain about placements in my college and then I joined Get SDE Ready. Their support and curriculum, along with live classes has helped me go forward in my career. I joined DSA & System Design batch and also took personal 1:1 mentorship with Arun that enabled me to perform really good in interviews.

I was not good in DSA during college and was looking for a job change. So I found your ad on Instagram and enrolled in DSA. I really liked the structured curriculum and the tests that were taken regularly. Because of the course, I have been able to crack 2 big companies: BharatPe and PayU.

I got placed in Kfintech on 8 LPA package after getting rejected from 8 companies in campus placement, i took the course in Jan 2024 and I got placed as a SOFTWARE ENGINEER in March 2024, your course helps me a lot in DSA as I was very very week in DSA. The structured curriculum and 24/7 doubt support helped me a lot.

Boost Your Career with a Course Completion Certificate

Earning a course completion certificate enhances your resume, demonstrating your commitment to learning and skill development. It increases your chances of getting hired by showcasing your expertise to employers. A certified qualification can set you apart from other candidates, improve your credibility, and open new job opportunities in your field.

What Our Students Are Saying

See our Students Journey here

How Miraj Cracked Multiple Job Offers with 65%+ Salary Hike! | DSA & System Design Success

How Miraj Cracked Multiple Job Offers with 65%+ Salary Hike! | DSA & System Design Success

How Akash Landed a Dream Job at Ecom Express DSA & System Design Success Story

How Akash Landed a Dream Job at Ecom Express DSA & System Design Success Story

How Shweta is able to crack offers from Walmart, Microsoft & Expedia with Get SDE Ready | Get SDE Ready Review

How Shweta is able to crack offers from Walmart, Microsoft & Expedia with Get SDE Ready | Get SDE Ready Review

How Akshita cracked Nokia and got 45% hike

How Akshita cracked Nokia and got 45% hike

Pawan got 150% hike

Pawan got 150% hike

Ankush cracked BharatPe & PayU | 250% hike with Get SDE Ready | Interview Questions discussed

Ankush cracked BharatPe & PayU | 250% hike with Get SDE Ready | Interview Questions discussed

How Raghu is able to crack 3 offers with Get SDE Ready | Get SDE Ready Review

How Raghu is able to crack 3 offers with Get SDE Ready | Get SDE Ready Review

Raghav Cracks Mastercard

Raghav Cracks Mastercard

Prabhat's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly! | Coding | Tech

Prabhat's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly! | Coding | Tech

Gourang's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly!

Gourang's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly!

Shubham's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly!

Shubham's Amazing Journey with Get SDE Ready: Transforming Tech Careers Seamlessly!

Student Feedback | Pranay on Our Mastering DSA Instructor Class

Student Feedback | Pranay on Our Mastering DSA Instructor Class

Frequently Asked Questions

Easily find answers to common questions about our courses and services

Who should join this course?

Our courses are intended for anyone aspiring to become a Software Development Engineer. This includes university students who have very basic programming knowledge (2nd year or above), working professionals looking for a career switch or trying to upskill themselves, and anyone interested in the field of software development.

Is the amount refundable if I don't like the course?

This course offers a no-questions-asked, full refund policy effective within seven days of its registration.

What is the mode of the course, online or offline?

The course is 100% online. All the classes are going to be 100% live.

What if I miss a class?

You will be getting recordings of your classes and you will have lifetime access to them.

Are there any prerequisites?

Only basic programming knowledge is required.

Will there be doubt classes?

We have 24/7 live doubt support. Also, we conduct live doubt classes regularly as well in the weekdays whose timings are from 9pm-11pm.

What is the duration of the course?

The course’s duration is expected to span between 5-6 months, though it can be extended if there is a need for additional classes focused on specific topics or further doubt sessions.

I have a full time job. Not sure if I will be able to make it?

The course is for working professional only. That’s why the timings are such: Instructor classes are on the weekends from 12pm-3pm. Doubt Classes are on the weekdays from 9pm-11pm.

Would there be any certificate on completion?

Yes! you will be getting certificate of completion.

Do you provide placement assistance?

Yes we provide placement assistance.

Its in 4 steps:

We will help you create a strong resume that increases your chances of getting shortlisted by companies.
⁠We will optimise your LinkedIn profile to ensure that recruiters can easily find and connect with you.
We will conduct mock interviews to help you assess your skills and prepare you to excel in real interviews.
We provide referrals to help you apply to top companies and get your interviews scheduled.

If I face any issues with my work or, due to an emergency, I am unable to attend the batch, can I join the next batch?

Yes, we completely understand that, as a working professional, you might face challenges. You can attend the live classes in any of the next three consecutive batches.

Want to receive push notifications for all major on-site activities?

✕