Unsupervised Learning in Machine Learning > Introduction to Machine Learning

Introduction to Machine Learning

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Machine learning is a branch of artificial intelligence that is defined as a machine's ability to mimic intelligent human behaviour. Artificial intelligence systems are utilised to complete complex jobs in a similar manner to how humans solve problems.

Machine learning is divided into three categories: supervised learning, unsupervised learning, and reinforcement learning.

The common ML workflow consists of five main tasks:

  • Obtain information. Obtaining data is the initial step in the Machine Learning process.
  • Data must be cleaned, prepared, and manipulated. Real-world data is frequently disorganized, missing, or noisy.
  • Train Simulator It's at this point when the magic happens!
  • Model for testing.
  • Improve.

Machine learning is utilised in many apps on our phones, including search engines, spam filters, websites that generate personalised recommendations, banking software that detects suspicious transactions, and speech recognition.

Machine Learning's Benefits:

  • Trends and patterns are easily discernible.
  • There is no need for human intervention (automation)
  • Improvement is a continuous process.
  • Managing data that is multi-dimensional and diverse.

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Prabhat Yadav
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Superb course content and easy to understand.

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Good Course

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Course Content

  1. Introduction to Machine Learning
    1m 3s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Categorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
  13. Plotting Graphs - Part 1
    9m
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    9m 49s

  1. Distance Metrics
    6m 2s
  2. K-Means Clustering
    11m 3s
  3. K-Means Clustering - Practical
    9m 49s
  4. Hierarchical Clustering - Agglomerative , Divisive
    4m 38s
  5. Agglomerative Clustering - Practical
    4m 44s
  6. Divisive Clustering - Practical
    14m 42s
  7. DBscan Spatial Clustering
    8m 49s
  8. FP Growth
    7m 43s
  9. Assignment of Unsupervised Learning Algorithms
    2m 41s

  1. Overview of Dimensionality Reduction
    4m 25s
  2. Principal Component Analysis
    33m 44s
  3. Princinpal Component Analysis - Practical
    6m 34s
  4. Linear Discriminant Analysis
    18m 51s
  5. Linear Discriminant Analysis - Practical
    6m 42s
  6. Assignment of Dimensionality Reduction
    55s

  1. Advance Trends in Machine Learning
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions part 1
    19m 22s
  2. Interview Questions part 2
    12m 23s
  3. Interview Questions part 3
    15m 20s

  1. Career Guidelines
    1m 28s
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