OVERVIEW OF THE COURSE #INTRODUCTION TO MACHINE LEARNING

• MACHINE LEARNING
• Relationship Between AI & ML?
• Why Machine Learning?
• Traditional Approach VS Machine Learning Approach
  1. Traditional Approach
  2. Machine Learning Approach

#INTRODUCTION TO MACHINE LEARNING

• MACHINE LEARNING
• Relationship Between AI & ML?
• Why Machine Learning?
• Traditional Approach VS Machine Learning Approach
  1. Traditional Approach
  2. Machine Learning Approach

hEAD 1	HEAD2	HEAD3
First	Second Column	Third Column
First	Second Column	Third Column
First	Second Column	Third Column

• Machine Learning Process

  1. Historical Data
  2. Feature Engineering
  3. Train Data
  4. ML Algorithm
  5. Test Data
  6. Model Validation
  7. Model
  8. New Data
  9. Results

• Applications of ML

1. Image Processing
2. Robotics
3. Data Mining
4. Video Games
5. Text Analysis
6. Healthcare

• Machine Learning Techniques

1. Classification
2. Categorization
3. Clustering
4. Trend Analysis
5. Anamoly Detection
6. Visualization
7. Decision Making

• HOW TO ADD IMAGE

  [Link_to_be_displayed]

  google

• HOW TO ADD TABLES

  • MACHINE LEARNING TYPES 1.Supervised Machine Learning
  1. Regression
  2. Classification
  • SUPERVISED MACHINE LEARNING ALGORITHMS
  1. Naive bayes classifers
  2. K-Nearest neighbors(KNN) Classifer
  3. decision tree
  4. ensemble learning algorithms
  5. support vector machine
  6. regression algoithms

  2.Unsupervised Machine Learning

  1. Clustering
  2. Association rules
  3. Dimensionality reduction.
  • UNSUPERVISED MACHINE LEARNING ALGORITHMS
  1. hierarichal clustering
  2. k-means clustering
  3. principal components analysis
  4. association rule mining
  5. DBSCAN
  • 3.Reinforcement Machine Learning

• Machine Learning Process

  1. Historical Data
  2. Feature Engineering
  3. Train Data
  4. ML Algorithm
  5. Test Data
  6. Model Validation
  7. Model
  8. New Data
  9. Results

• Applications of ML

1. Image Processing
2. Robotics
3. Data Mining
4. Video Games
5. Text Analysis
6. Healthcare

• Machine Learning Techniques

1. Classification
2. Categorization
3. Clustering
4. Trend Analysis
5. Anamoly Detection
6. Visualization
7. Decision Making

• HOW TO ADD IMAGE

  Link_to_be_displayed

  google

• HOW TO ADD TABLES

  • MACHINE LEARNING TYPES 1.Supervised Machine Learning
  1. Regression
  2. Classification
  • SUPERVISED MACHINE LEARNING ALGORITHMS
  1. Naive bayes classifers
  2. K-Nearest neighbors(KNN) Classifer
  3. decision tree
  4. ensemble learning algorithms
  5. support vector machine
  6. regression algoithms

  2.Unsupervised Machine Learning

  1. Clustering
  2. Association rules
  3. Dimensionality reduction.
  • UNSUPERVISED MACHINE LEARNING ALGORITHMS
  1. hierarichal clustering
  2. k-means clustering
  3. principal components analysis
  4. association rule mining
  5. DBSCAN
     3.Reinforcement Machine Learning

  | Serial no | Title | LINK | | ------ | ----- | ----- | | 1 | PYTHON PROGRAMS | assignment1 | | 2 | NUMPY EXERCISES | Third Column | | 3 | PANDAS EXERCISES | Third Column |