Machine Learning

Training Milestones

Book Counselling Session

Module 01:- Recap of Data Analysis:-

Foundation of Machine Learning: Brush up on the essentials of data analysis and set the stage for advanced machine learning techniques.

    Essentials Covered:-

  1. 1. Data Analysis Unveiled: Understanding its role in today’s AI-driven world.
  2. 2. AI Landscape: A bird’s eye view of artificial intelligence and its components.
  3. 3. Statistical Basics: Core terminologies that fuel data interpretations.
Module 02:- Introduction to Machine Learning

Unraveling the Machine Learning: Mystique Dive into the diverse types of machine learning and understand how algorithms learn from data.

    Essentials Covered:-

  1. 1. Machine Learning Defined: From theory to practical applications.
  2. 2. Learning Types: Exploring Supervised vs Unsupervised Learning.
  3. 3. ML at Scale: Broader perspectives on applying machine learning.
  4. 4. Workflows and Pipelines: Streamlining the machine learning process for efficiency.
Module 03:- Outlier Ananlysis

Detecting the Anomalies: Learn to identify and analyze outliers that can dramatically skew the data you work with.

    Essentials Covered:-

  1. 1. Significance of Outliers: Impact on datasets and decisions.
  2. 2. Detecting Outliers: Techniques for identifying data anomalies.
  3. 3. Analysis Strategies: Tools and methods for outlier investigation.
Module 04:- Dimentionality Reduction

Simplifying Complexity: Address the curse of dimensionality by learning powerful techniques to reduce data dimensions while retaining essential information.

    Essentials Covered:-

  1. 1. Understanding Dimensionality: Challenges of high-dimensional data.
  2. 2. PCA: A refresher on Principal Component Analysis.
  3. 3. K-Means, DBSCAN, Hierarchical Clustering: Grouping data.
  4. 4. t-SNE: Exploring advanced visualization with t-distributed Stochastic Neighbor Embedding.
Module 05:- Forecasting

Predicting the Future: Master the art of forecasting using both traditional statistical models and innovative machine-learning approaches.

    Essentials Covered:-

  1. 1. Forecasting Foundations: Predictive analytics in a nutshell.
  2. 2. Statistical Models: Dive into ARIMA, ETS, and VAR.
  3. 3. Machine Learning Integration: Enhancing forecasts with machine learning techniques.
  4. 4. Autoregression Techniques: Delve into models that predict future values based on past data
Module 06:- Working with Text Data

Unlocking the Power of Words: Explore how machine learning can extract meaningful insights from text, from social media analysis to advanced NLP applications.

    Essentials Covered:-

  1. 1. Text Data Exploration: Introduction to handling and analyzing text.
  2. 2. Data Collection: Techniques for gathering text from diverse sources, including social media.
  3. 3. NLP Basics: An overview of Natural Language Processing with tools like Spacy and NLTK.
  4. 4. Deep Dive into Embeddings: Learn how vector embeddings transform text into analyzable data.
Module 07:- Databases

    Essentials Covered:-

  1. Introduction to Vector Databases