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Machine Learning

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Zero to Master Program

Machine Learning

Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It involves the development of algorithms that can identify patterns and make decisions based on data. ML techniques range from simple linear regression to complex neural networks and deep learning. Applications of ML span various fields, including healthcare, finance, and autonomous systems. By leveraging large datasets and computational power, ML models can perform tasks such as image and speech recognition, natural language processing, and predictive analytics. The goal of ML is to create systems that can adapt and perform tasks with increasing accuracy over time. As data availability and computational resources grow, the impact and potential of ML continue to expand.

Fee

COURSE FEE

₹15000

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4.8

12K+ Learners enrolled

100 +

Duration(Hours)

350+

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About

Curriculum

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FAQ

This is where you embark on an amazing journey!

Most flexible program in the industry

Freedom to learn

Watch classes any time at your convenience

Cheat days

Catch up on the course when life is calling you elsewhere

Features that keep you going

A structured curriculum that makes learning easy

Practice code problems of varying difficulty

Engagement coach to keep you motivated

Compile & run in an integrated coding environment

Get doubts resolved in 30 mins

1:1 sessions over voice call & chat with our skilled teaching assistants

Industry leading mentors to help you grow

1:1 Mock interviews with resume and career guidance

Structured feedback to make you better

Get a chance to be referred to your mentors’ company

Experience a seamless job switch with hiring assistance

Skill-based hiring across all levels of experience

The results

110%

Average salary hike

7000+

Transitions to product companies

250+

Trusted placement partners

Frequently asked but seldom read questions

Yes, this course is entirely offline, and we offer some courses online as well.

Yes, there will be optional introductory sessions on Python programming at the beginning of the course. Additional resources and help will be available throughout the course to support students new to programming.

This course will equip you with essential skills in machine learning that are highly sought after in various industries such as tech, finance, healthcare, and more. You will gain hands-on experience with real-world data and projects, making you well-prepared for roles such as Data Scientist, Machine Learning Engineer, and AI Specialist.

This course will equip you with the skills needed to build and apply deep learning models, which are in high demand in various industries such as technology, healthcare, finance, and more. It can open up career opportunities in roles such as Data Scientist, Machine Learning Engineer, AI Researcher, and more.

The course is divided into modules, each focusing on a specific topic. Each module includes lectures, readings, practical assignments, and quizzes. There are also projects that integrate multiple concepts learned throughout the course.
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Meet the faculty legends that will make you legendary

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Praveen Kumar

Founder & Instructor

Praveen has a full stack development experience and professional instructor and trainer for Flutter, Data Science, Machine Learning and Python Programming. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations.

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Lenin Prakash

Co-Founder & Instructor

He is an expert in JavaScript & React (Front-end) and has worked on open-source projects like Firebug and Zulip. He has also served as a GCI (Google Code-In) Mentor with Zulip. In his previous role as a Software Engineer he has worked for Goibibo-MMT.

Course curriculum for the curious

  • -Introduction to Machine Learning and its types (supervised, unsupervised, reinforcement learning)
  • -Setting up the development environment (Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn)
  • -Overview of the Machine Learning workflow and common data preprocessing techniques
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  • -Definition of data science and its role in various industries.
  • -Explanation of the data science lifecycle and its key stages.
  • -Overview of the different types of data: structured, unstructured, and semi-structured
  • -Discussion of the importance of data collection, data quality, and data preprocessing.
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  • -Introduction to Pandas
  • -Overview of NumPy
  • -Explanation of key data structures in Pandas
  • -Hands-on exploration of data using Pandas to summarize, filter, and transform data
  • -Data cleaning techniques, handling missing values, and dealing with outliers
  • -Statistical analysis of data using NumPy functions
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  • -Introduction to Data Visualization and its importance in data analysis.
  • -Overview of Matplotlib
  • -Exploring different types of plots
  • -Customizing plots with labels, titles, colors, and styles.
  • -Introduction to Seaborn
  • -Advanced plotting techniques with Seaborn
  • -Introduction to Plotly
  • -Creating interactive and dynamic visualizations with Plotly
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  • -Introduction to Data Engineering
  • -Data cleaning and Handling missing values
  • -Feature Engineering techniques
  • -Data Scaling and Normalization
  • -Dealing with categorical variables
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  • -Introduction to web scraping
  • -Scraping data from websites using libraries like BeautifulSoup and requests
  • -HTML parsing, locating elements, and extracting data
  • -Handling different types of data on websites
  • -Storing scraped data in appropriate formats: CSV, JSON, or databases
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  • -Introduction to Regression
  • -Linear Regression
  • -Polynomial Regression
  • -Lasso and Ridge Regression
  • -Evaluation metrics for regression models
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  • -Introduction to Classification
  • -Logistic Regression
  • -Decision Trees
  • -Random Forests
  • -Evaluation metrics for classification models
  • -Implementation of classification models using scikit-learn library
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  • -Support Vector Machines (SVM)
  • -K-Nearest Neighbors (KNN)
  • -Naive Bayes
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  • -AdaBoost
  • -Gradient Boosting (XGBoost)
  • -Evaluation and fine-tuning of ensemble models
  • -Handling imbalanced datasets/h5>
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  • -Introduction to Clustering
  • -K-means Clustering
  • -DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • -Evaluation of clustering algorithms
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  • -Introduction to Dimensionality Reduction
  • -Principal Component Analysis (PCA)
  • -Implementation of PCA using scikit-learn library
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  • -Cross-validation and model evaluation techniques
  • -Hyperparameter tuning using GridSearchCV and RandomizedSearchCV
  • -Model selection and comparison
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  • -Introduction to NLP
  • -Text Preprocessing
  • -Text Representation
  • -Sentiment Analysis
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  • -Introduction to Recommendation Systems
  • -Collaborative Filtering
  • -Content-Based Filtering
  • -Deployment and Future Directions
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  • -Introduction to Reinforcement Learning: Agent, environment, state, action, and reward
  • -Markov Decision Processes (MDP)
  • -Q-Learning algorithm
  • -Hands-on reinforcement learning projects and exercises
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  • -Introduction to Flask / Streamlit web framework
  • -Creating a Flask / Streamlit application for ML model deployment
  • -Integrating data preprocessing and ML model
  • -Designing a user-friendly web interface
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  • -Building a web application for Machine Learning models
  • -Deployment using AWS (Amazon Web Services)
  • -Deployment using PythonAnywhere
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