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Machine Learning Specialization

Price

Duration

₹15,000

Monthly

20 Weeks

The ML Specialization is a 36-week journey designed to equip learners with the core skills, tools, and mindset required to thrive in the AI-driven world. From foundational programming to cutting-edge deep learning, this course builds step-by-step expertise across the entire machine learning pipeline.


Learners will master Python programming, gain a strong grasp of the mathematical foundations behind ML, and explore both classical algorithms and modern deep learning techniques. Specialized modules in computer vision, natural language processing, and large language models (LLMs) prepare students to build intelligent systems that solve real-world problems.


What sets this specialization apart is its hands-on, project-driven approach, designed to make learning both joyful and rigorous, while fostering an agentic, future-ready mindset.

Topics

  • Python and Math for Machine Learning

    1. Python programming fundamentals

    2. Essentials of Linear Algebra, Probability, Statistics, Differential Calculus

    3. Dask, PyTorch, Bokeh for large data handling and large data visualizations

    4. Writing clean, modular, and efficient code

  • Foundations of Machine Learning

    1. Supervised, unsupervised and reinforcement Learning

    2. Model selection and evaluation

    3. Feature engineering & data preprocessing

    4. Cost functions and gradient descent

    5. Key algorithms: Multiple linear regression, Logistic regression, Decision trees, k-Means, PCA

  • Data Analysis

    1. Exploratory Data Analysis (EDA)

    2. Handling missing values, outliers, and noise

    3. Data visualization with Bokeh

    4. Feature types, distributions, correlation, and trends

    5. Working with time-indexed data

    6. Trend, seasonality, autocorrelation

    7. Forecasting models: ARIMA, Prophet


  • Deep Learning & Computer Vision

    1. Neural networks and backpropagation

    2. Convolutional Neural Networks (CNNs)

    3. Image classification, object detection, transfer learning

    4. Tools: PyTorch


  • Deep Learning for NLP

    1. Text preprocessing and embeddings

    2. Very brief history of NLP field

    3. Transformers and Attention mechanism

    4. Sequence modeling, sentiment analysis, text generation


  • Local LLM and Prompt Engineering

    1. Working in LLMs locally

    2. Types of LLMs and their applications

    3. Advance prompt engineering


  • Capstone

    1. Project proposals

    2. Team formation, work distribution

    3. Development and hosting

    4. Demo


Prerequisite:

  1. Learner's Mindset

  2. Laptop with 16 GB RAM (Higher is better) to try basic things locally before moving on to cloud

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