1. Introduction to Machine Learning -

1.1 What is Machine Learning?

1.2 Machine Learning Use-Cases

1.3 Machine Learning Process Flow

1.4 Machine Learning Categories

1.5 Linear regression

 

2. Introduction to AI/Machine Learning Libraries -

2.1 NumPy - arrays

2.2 Operations on arrays

2.3 Indexing slicing and iterating

2.4 Reading and writing arrays on files

2.5 Pandas - data structures & index operations

2.6 Reading and Writing data from Excel/CSV formats into Pandas

2.7 matplotlib library

2.8 Grids, axes, plots

2.9 Markers, colours, fonts and styling

2.10 Types of plots - bar graphs, pie charts, histograms

2.11 Contour plots

 

3. Supervised Learning -

3.1 What are Classification and its use cases?

3.2 What is Logistic regression?

3.3 What is Decision Tree?

3.4 What is Random Forest?

3.5 What is Naïve Bayes?

3.6 What is Support Vector Machine?

3.7 Confusion Matrix

3.8 Hyperparameter Optimization

3.9 Grid Search vs Random Search

 

Hands-On/Demo:

. Implementation of -

. Logistic regression

. Decision tree

. Random forest

. Naïve Bayes

. SVM Skills:

. Supervised Learning concepts

. Implementing different types of Supervised Learning algorithms

. Evaluating model output

. Supervised Learning concepts

. Implementing different types of Supervised Learning algorithms

. Evaluating model output

 

4. Dimensionality Reduction -

4.1 Introduction to Dimensionality

4.2 Why Dimensionality Reduction

4.3 PCA

 

Hands-On/Demo:

. PCA Skills:

. Implementing Dimensionality Reduction Technique

 

5. Unsupervised Learning -

5.1 What is Clustering & its Use Cases?

5.2 What is K-means Clustering?

5.3 How does K-means algorithm work?

 

Hands-On/Demo:

. Implementing K-means Clustering

. Implementing Hierarchical Clustering Skills:

. Unsupervised Learning

. Implementation of Clustering

 

6. Association Rules Mining -

6.1 What are Association Rules?

6.2 Association Rule Parameters

6.3 Calculating Association Rule Parameters

 

Hands-On/Demo:

. Apriori Algorithm Skills:

. Data Mining using python

 

7. Model Selection and Boosting -

7.1 What is Model Selection?

7.2 Requirement of Model Selection

7.3 Cross-Validation

7.4 What is Boosting?

7.5 How Boosting Algorithms work?

7.6 Types of Boosting Algorithms

7.7 Adaptive Boosting Hands on/Demo:

. Cross-Validation

. AdaBoost Skills:

. Model Selection

. Boosting algorithm using python