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Machine Learning

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  • Data-Driven Model Development
  • Multiple Learning Techniques
  • Hands-On Model Training
  • Algorithm Implementation
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Machine Learning
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Overview

Machine Learning enables systems to analyze large datasets, identify patterns, and generate accurate predictions through data-driven models. It plays a key role in building intelligent applications by continuously improving performance based on new data. Machine learning is widely applied in areas such as predictive analytics, recommendation systems, fraud detection, automation, and intelligent decision-making across industries.

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Data Science Course Syllabus

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Machine Learning Prerequisites - Python Basics

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  • Get introduced to basic programming terminologies and concepts like variables, data types, operators, functions, etc.
    00:00:00

    Get introduced to basic programming terminologies and concepts like variables, data types, operators, functions, etc.

  • Understand the implementation of the object-oriented programming (OOPS) concept.
    00:00:00
  • By the end of this module, you should be able to create programs in Python
    00:00:00
  • Understand and Implement subqueries, rules, views, pattern matching, UDFs, stored procedures, etc.
    00:00:00

    Understand and Implement subqueries, rules, views, pattern matching, UDFs, stored procedures, etc.

  • Optimise your SQL queries by indexing, grouping, sorting, and CTEs(Common Table Expressions).
    00:00:00
  • Learn to manipulate data and create interactive visualisations using Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.
    00:00:00

    Learn to manipulate data and create interactive visualisations using Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.

  • Understand essential preprocessing techniques, such as exploratory data analysis, feature engineering, scaling, normalisation, and standardisation.
    00:00:00
  • Using Python libraries, create bar charts, scatter plots, count plots, line plots, pie charts, doughnut charts, and more.
    00:00:00
  • Understand descriptive statistical measures such as central tendency, spread, five-point summary, etc.
    00:00:00

    Understand descriptive statistical measures such as central tendency, spread, five-point summary, etc.

  • Learn about inferential statistical measures such as correlation, covariance, confidence intervals, hypothesis testing, F-test, Z test, t-test, ANOVA, and chi-square test.
    00:00:00
  • Analyse the probability of an event using probability distribution, Bayes’s theorem and central limit theorem.
    00:00:00
  • Get Introduced to machine learning, its types and usage.
    00:00:00

    Get Introduced to machine learning, its types and usage.

  • Learn about regression, classification and clustering. Learn how to pick the right algorithms for a given problem statement.
    00:00:00
  • Create a machine learning model for each of them and understand how to train, evaluate, and optimise them.
    00:00:00
  • Deep Dive into different machine learning algorithms and understand their working, importance and usage.
    00:00:00

    Deep Dive into different machine learning algorithms and understand their working, importance and usage.

  • Learn and Implement supervised machine learning algorithms such as Linear regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Descent, K- nearest neighbours, and Time Series Forecasting.
    00:00:00
  • Learn and Implement different unsupervised machine learning algorithms, such as K-means, Dimensionality reduction, linear discriminant analysis, Principal Component Analysis, etc.
    00:00:00
  • Understand and learn the basics of Artificial Intelligence and Python libraries like Keras and TensorFlow.
    00:00:00

    Understand and learn the basics of Artificial Intelligence and Python libraries like Keras and TensorFlow.

  • Learn the essential concepts of neural networks, such as their definition, advantages, structure, usage, types, etc.
    00:00:00
  • Implement crucial deep learning concepts, such as Deep neural networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), GPUs in deep learning, Autoencoders, Restricted Boltzmann machines, etc.
    00:00:00
  • Explore the fundamentals of generative AI like LSTM, transformer architecture, BERT, etc
    00:00:00

    Explore the fundamentals of generative AI like LSTM, transformer architecture, BERT, etc

  • Understand why these Gen AI models are better than traditional machine learning models.
    00:00:00
  • Implement and learn about the evolution of large language models (LLMs) and their architecture to better understand context and perform complex generation tasks.
    00:00:00
  • Understand the basic concepts of NLP, such as tokenisers, POS tagging, lemmatisation, trigrams, Ngrams, text classification, stemming, frequency distribution, etc.
    00:00:00

    Understand the basic concepts of NLP, such as tokenisers, POS tagging, lemmatisation, trigrams, Ngrams, text classification, stemming, frequency distribution, etc.

  • Work with language models, sequencing tasks, syntax trees, chunking, chinking, context-free grammar, etc.
    00:00:00
  • Learn about the mechanism of words, term frequency, text conversion, count vectoriser, inverse document, etc.
    00:00:00
  • Learn the basics of Prompt Engineering.
    00:00:00

    Learn the basics of Prompt Engineering.

  • Explore various strategies for prompt design.
    00:00:00
  • Understand methods for evaluating prompt effectiveness and applying feedback loops to optimise the output.
    00:00:00
  • Understand the fundamental workings of MLOps and its lifecycle, from developing the model to evaluating and deploying it over Azure Machine Learning.
    00:00:00

    Understand the fundamental workings of MLOps and its lifecycle, from developing the model to evaluating and deploying it over Azure Machine Learning.

  • Get introduced to Microsoft Azure Machine Learning service and deploy your first machine learning model on it.
    00:00:00
  • You will start by extracting and loading the data, followed by transforming it into a format suitable for driving inferences.
    00:00:00

    You will start by extracting and loading the data, followed by transforming it into a format suitable for driving inferences.

  • The next step will be manipulating and preprocessing the data to prepare it for creating a model
    00:00:00
  • Before creating the machine learning model, we must perform feature engineering related to the problem statement.
    00:00:00
  • Then, we will select a suitable model that fits the problem statement and the data, train it with the training dataset, and test it using the testing dataset.
    00:00:00
  • The last step is to evaluate the model and monitor it so that it gives a proper outcome.
    00:00:00

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