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Why Learn Machine Learning (ML)?

In a nutshell, Machine learning helps us automate rules or decision-making processes with the help of mathematics, code, and data. Various Industries are now using Machine Learning Algorithms to increase their revenue by making predictions about their customer's behavior and upcoming trends in the market. Automatic Recommendations in your mobile keyboard and e-commerce websites, Google Voice Assistant, Self-driving Cars, and stock prediction are some examples that are built using machine learning. A critical aspect of machine learning is to train the model with the help of available data to get maximum accuracy when deployed to solve real-world problems.
Globally, Machine Learning Engineering is one of the most sought-after, in-demand profession, and almost all industries are actively hiring ML engineers, so obtaining a Machine Learning skill is highly recommended In this fast-growing field, Scaler is offering a Machine Learning & Data Science training course designed for people belonging to all expertise levels - beginners, intermediate and experts. After you complete this Machine Learning course, you will have an in-demand set of skills that are critical to today's career opportunities in:
  • Machine Learning Engineer
  • Human-Centered Machine Learning Designer
  • NLP Scientist
  • Junior/Senior/Associate ML Scientist
  • Portfolio Risk Data Scientist
  • A machine learning engineer is a highly qualified professional who uses machine learning and software development skills to build and design scalable machine learning systems that can automate predictive models . They shall have good knowledge of system design, Machine Learning Algorithms , Development and Statistics. The primary responsibility of ML Engineer includes,
  • The ML models are performing optimally with the help of the input dataset.
  • Understanding all the features of a model and implementing it correctly.
  • Knows how to scale and deploy Machine learning models.
  • Understand the math behind the machine learning model.
  • Work on Machine Learning projects built in partnership with top companies

    Work on real industry projects, get real-time feedback from mentors, and interact with your peers to discuss different solutions during live classes. Here are a few of the sample projects:
    Estimate food arrival time at customer's location.
    Use data to plan the best strategy for offering discount coupons.
    Detect fraud by analyzing millions of chat messages.
    Experiment with different driving routes to reach destination in minimum time interval.
    *Projects may be updated based on student and partner feedback.

    Get 1:1 guidance with our top industry mentors from the Machine Learning Industry

    We have more than 300 mentors who have already guided thousands of students by taking their mock interviews, providing them detailed feedback and helping them to define their career path. Scaler provides 1:1 mentoring sessions.

    Our panel of Industry Advisors at Scaler have helped in creating best-in-class Data Science & Machine Learning Training Program.

    Pawan Kumar
    Head of Data Science, Uber
    Bhavik Rasyara
    Boston Consulting Group
    Yash Mimani
    McKinsey & Company
    Ramit Sawhney
    Tower Research Capital / ShareChat

    Curriculum is designed to make you a solid Data Scientist

    Beginner
    15 Months
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    Intermediate
    11 Months
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    Advanced
    7 Months
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    Module - 1

    Beginner Module

    4 Months
    Not required for Intermediate
    Module - 2

    Data Analysis and Visualization

    4 Months
    Not required for Intermediate
    Module - 3

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 4

    Specializations

    3 Months
    Module - 5

    Machine Learning Ops

    1 Month
    Module - 6

    Advanced Data Structures and Algorithms

    4 Months
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1:Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2:Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1:Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2:Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum

    Our Tutors & Instructors will make you confident about the fundamentals required for Machine Learning

    Our tutors are experts from top companies who have built scalable systems and understand the real-life importance of Data Science and Machine Learning.
    Connect with an Academic Counsellor
    Get all the information about scholarships and low cost EMI options
    Eligibility: Anyone who graduated in 2022 or before

    We also help you prepare for your job search and your interviews

    Exchange job opportunities with our 20,000+ Scaler student community.
    Access job opportunities from more than 180 employer partners of Scaler
    Get ready for job interviews by practicing mock interviews with industry professionals.
    Keep your resume & LinkedIn profile optimized with guidance of our experts

    Scaler's alumni work at reputed tech organizations and promising startups

    Hear from our scaler students and make an informed decision!

    I am superpysched to be a part of Tekion Corp as Senior Software Engineer. The best of interview process I have ever experienced.
    Thanks to the entire team of Scaler to help upgrade my skillsets which certainly made it less daunting. Anshuman Singh Abhimanyu Saxena. Hope to take it to even greater heights!
    The way they taught was the aspect that stood out most to me. Each student was invited to give solutions, and every answer was dissected. No doubt was small enough, and every approach was thought about.
    Thanks to scaler lectures, mentor sessions, and my Mentor Drishti Agarwal, who not only helped me in tackling technical questions but also constantly gave me behavioural and communication tips.
    And the most important part, the mock interviews with my mentor, made me more confident day by day.

    Tuition Fee

    With EMI options, your payment can be as low as Rs 9,811 per month - that's like your monthly grocery bill!
    Total fee: Rs 2.99L inclusive of GST at super affordable EMI options. Try the course for the first 2 weeks - full money-back guarantee if you choose to withdraw.
    EMIs (Pay in Equated Monthly Installments)

    You can find both no-cost EMI & standard interest EMI from our NBFC partners. See below a summary of their best plans (more details available at the time of payment)

    emi-image

    Delievered via our EMI partners - ZestMoney, Propelled, Eduvanz, Liquiloans

    You can also choose to avail EMI options from your credit card providers.

    Machine Learning Training Course FAQs

    Who should take this Machine Learning course program?

    Anyone who wants to learn machine learning and make a career in it, whether they are beginners or professionals, are welcome to enroll for this Data Science & Machine Learning Course. Software, IT, and marketing professionals can enroll part-time or through external programs in Machine Learning.

    What kind of jobs or career opportunities are present in the Machine Learning domain?

    In Machine Learning, there are a variety of job roles that can be assigned depending on industry needs. Once you finish our Machine Learning course, you will possess an in-demand set of skills critical to today's career opportunities, which include: Machine Learning Scientist, Machine Learning Engineer, Human-Centered Machine Learning Designer, Research Analyst, Data Analytics, Analytics Lead/Manager/Consultant, Statistical Programming Specialist, Hadoop Developer, and many more.

    What kind of salary can I expect as a Machine Learning Professional?

    As a fresher in Machine Learning Engineer, you can expect to earn between Rs. 4-8 lakhs per annum. An experienced professional data scientist (3-5 years experience) makes more than Rs. 10 lakhs per annum by virtue of their skills and expertise. Their skill development and learning capabilities largely determine their salary increases. If you are more adept at learning, you will be more likely to grow.

    What is the eligibility for Scaler's Machine Learning and Data Science course?

    As a subject, Machine Learning is a fusion of concepts from Mathematics, Computer Science, and Statistics. It is essential that students have at least one degree in one of the following fields: science, technology, engineering, or mathematics. Additionally, having studied computer programming in high school can be beneficial.

    How can Scaler's Data Science and Machine Learning course assist me in building a successful career in Machine Learning?

    A machine learning engineer must possess a wide range of skills and know the tools and techniques involved. Some of the key skills you learn with our machine learning course include statistical skills, mathematical skills, programming skills (Python, Java, etc.), Data mining skills, cloud computing skills (like AWS and others), ML skills (K-Nearest Neighbour, random forests, ensemble methods, support vector machines, etc.), database skills (SQL, NoSQL, etc.), etc

    What are the fees for the Data Science and Machine Learning course?

    The Data Science and Machine learning course fee in Ba is Rs. 2.5L inclusive of GST. EMI options are available. Take the course for two weeks - if you decide to withdraw, you're fully refunded. If you opt for an EMI option, your monthly payment will be as low as Rs. 7,200.

    Which companies hire Machine Learning Engineers?

    Almost all of the big companies like Google, Amazon, Facebook, Netflix have a requirement of good Machine Learning engineers. Machine learning is being applied in almost all the industries where data can be used to make predictions be it healthcare, IT, finance.

    What are the applications of Machine Learning?

    Some of the major applications of machine learning include speech recognition, self-driving cars, product recommendations, stock market trading, online fraud detection, email spam filtering, chatbots and voice assistance.

    Is the Machine Learning course difficult to learn?

    No. We have excellent instructors and mentors with us who will make the course very easy to learn and understand.

    What is the best coding language to learn Machine Learning?

    Python and R are the most used languages in Machine learning.

    Program Registration
    Thanks for your interest. We will let you know when the course is about to begin.