Machine Learning courses - Aussie Digital Business SystemsAussie Digital Business Systems Machine Learning courses - Aussie Digital Business Systems

Machine Learning courses

Syllabus for Short Term Course

Topic

Duration

Programming in PYTHON
    • Introduction to Python, IDLE
    • Control Flow
    • Functions
    • Lists, Tuples and Dictionaries

5 days
1 day examination
(Objective Type +
practical)

Intro’ to Statistics
Errors & scales of measurements, Presentation of data, Measures of variations & central tendency, Correlation & regression, Probability & hypothesis, Exploratory Data Analysis, Parametric & Non- Parametric tests

2 days

Linear Algebra Review
    • Matrices & Vectors
    • Matrix Vector Multiplication
    • Matrix matrix Multiplication
    • Inverse and Transpose

2 days

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Anaconda Explorer Using Python (following topics) Data Pre-processing
    • Importing the dataset
    • Missing Data
    • Outlier Detection
    • Categorical Data
    • Splitting the dataset into the Training set and Test set.
    • Feature Scaling

4 days

Regression
    • Simple Linear Regression
    • Simple Linear Regression in Python
    • Multiple Linear Regression
    • Multiple Linear Regression in Python
    • Polynomial Regression
    • Polynomial Regression in Python
    • Decision Tree Regression
    • Decision Tree Regression in Python
    • Random Forest Regression
    • Random Forest Regression in Python
    • Evaluating

4 days
1 day examination
(Objective Type +
practical)

Classification
    • Logistic Regression
    • Logistic Regression in Python
    • K-Nearest Neighbors (KNN)
    • K-NN in Python 
    • Support Vector Machine (SVM)
    • SVM in Python
    • Kernel SVM
    • Kernel SVM in Python
    • Naive Bayes
    • Naive Bayes in Python
    • Decision Tree Classification
    • Decision Tree Classification in Python
    • Random Forest Classification
    • Random Forest classification in Python
    • Evaluating Classification Models Performance

5 days
1 day examination
(Objective Type +
practical)

Clustering
    •K-Means Clustering
    •K-Means Clustering in Python
    •Hierarchical Clustering
    •Hierarchical Clustering in Python

3 days

Intro’ to Deep Learning
    •Artificial Neural Networks
    •Artificial Neural Networks in Python

 

4 days

Data Pre-processing contd. Dimensionality Reduction
    •Principal Component Analysis (PCA)
    •PCA in Python
    •Linear Discriminant Analysis (LDA)
    •LDA in Python

2 days

analysis

Big Data Technology
Distributed, Computing & Big data, IoT Data Acquisition, Big Data Management, Data aggregation, Big data processing with database and programming concepts

3 days

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Hadoop, Hive, No SQL

3 days
1 day examination

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Javascipt

4 days
1 day Examination

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Programming in R

4 days
1 day examination

R Studio Using R (following topics) Data Pre-processing
    •Importing the dataset
    •Missing Data
    •Outlier Detection
    •Categorical Data
    •Splitting the dataset into the Training set and Test set.
    •Feature Scaling

2 days

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Regression
    •Simple Linear Regression in R
    •Multiple Linear Regression in R
    •Random Forest Regression in R

2 days
1 day examination
(Objective Type +
practical)

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Classification
    •Logistic Regression in R
    •SVM in R
    •Kernel SVM in R
    •Random Forest classification in R

3 days
1 day examination
(Objective Type +
practical)

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Clustering
    •K-Means Clustering in R
    •Hierarchical Clustering in R

2 days
1 day examination

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Intro’ to Deep Learning in R
Artificial Neural Networks in Python

1 days

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Data Pre-processing contd. Dimensionality Reduction
    •PCA in R
    •LDA in R

 days

analysis

Our location

Unit 903, 227 Collins Street Melbourne, VIC – 3000. Australia

Email

sales@adbsystems.com.au

Phone

1300 784 249