Machine Learning Using Python

Certificate Course in Machine

Learning Using Python

Become a Machine Learning specialist,

gain holistic knowledge in ML algorithms.

*32 Hours Classroom & Live Online Sessions

*80+ Hours Assignments & Real-Time Projects

*IBM Digital Certificates and Badges

*Complimentary Python beginners course

Certificate Course

in

Machine Learning

Using Python

Become a Machine

Learning specialist,

gain holistic knowledge in

ML algorithms.

*32 Hours Classroom &

Live Online Sessions

*80+ Hours Assignments &

Real-Time Projects

*IBM Digital Certificates

and Badges

*Complimentary Python

beginners course

Master the fundamentals of Machine Learning using Python and deep-dive into the world of analytics and ML.

In this course, we focus on the field of ML and describe the well-known processes, algorithms, and tools for one to be a successful ML practitioner. Students will be able to build skills in data acquisition and modelling, classification, and regression. In addition, students will also get to explore highly essential tasks such as model validation, optimization, scalability, and real-time streaming.

Duration

5 days / 40 hours

Level

Beginner to Intermediate

Delivery

100% Online - Instructor Led

Course Code

ML101
Audience

Anyone with interest in Machine Learning And Python
Language

English
Certification

Awarded by ICTC

Machine Learning Course Overview

A rapidly growing field within Artificial Intelligence (AI), Machine Learning allows machines or computers to learn from huge volumes of complex data and self-improve. It powers technologies like recommendation engines, speech recognition (voice-driven interactions with machines), facial recognition, fraud protection, self-driving cars (autonomous vehicles), and an increasing number of exciting developments that are just now becoming realized. With more and more large and medium-sized enterprises adopting or intensifying their usage of machine learning, the demand for ML skills

Tools Covered For This Course:

Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

Become familiar with analyzing data, computing statistical measures along with Data Wrangling, Data Cleansing, Data Manipulation, etc.
Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine.
Become familiar with Regression algorithms and the application of Python, R as statistical software in Machine Learning and Data Science.
Build predictive models.
Be able to create Data Visualization, Data Manipulation in different forms and draw meaningful business insights from the underlying data.

Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

Become familiar with analyzing data, computing statistical measures along with Data Wrangling, Data Cleansing, Data Manipulation, etc.
Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine.
Become familiar with Regression algorithms and the application of Python, R as statistical software in Machine Learning and Data Science.
Build predictive models.
Be able to create Data Visualization, Data Manipulation in different forms and draw meaningful business insights from the underlying data.

Course Modules Covered in the Machine Learning Using Python program

Day 1 - An Overview of Python, Running Python Scripts, Getting Started, Flow Control, Sequence Data

An Overview of Python

1. What is Python?
2. Interpreted languages.
3. Advantages and disadvantages.
4. Downloading and installing.
5. Which version of Python.
6. Where to find documentation.

Running Python Scripts

1. Structure of a Python script
2. Using the interpreter interactively
3. Running standalone scripts under Unix and Windows

Getting Started

1. Using variables
2. String types: normal, raw and Unicode
3. String operators and expressions
4. Math operators and expressions
5. Writing to the screen
6. Command line parameters
7. Reading from the keyboard

Flow Control

1. About flow control
2. Indenting is significant
3. The if statements
4. The elif statements
5. While loops
6. Using lists
7. Using the for statement
8. The range() function

Sequence Data

1. list operations
2. list methods
3. Strings are special kinds of lists
4. Tuples
5. Sets
6. Dictionaries

Day 2 - Defining Functions, Working with Files, Dictionaries and Sets, Errors and Exception Handling, Using Modules

Defining Functions

1. Syntax of function definition
2. Formal parameters
3. Global versus local variables
4. Passing parameters and returning values

Working with Files

1. Text file I/O overview
2. Opening a text file
3. Reading text files
4. Raw (binary) data
5. Using the pickle module
6. Writing to a text file
7. Opening Excel File
8. Reading from Excel File
9. Writing data into Excel File

Dictionaries and Sets

1. Dictionary overview
2. Creating dictionaries
3. Dictionary functions
4. Fetching keys or values
5. Testing for existence of elements
6. Deleting elements

Errors and Exception Handling

1. Dealing with syntax errors
2. Exceptions
3. Handling exceptions with try/except
4. Cleaning up with finally

Using Modules

1. What is a module?
2. The import statement
3. Function aliases
4. Packages
5. Standard Modules – sys
6. Standard Modules – math
7. Standard Modules – time

Day 3 - Regular Expressions, Highlights of the Standard Library, Python Classes, Python for Data Analysis - NumPy, Python for Data Analysis – SciPy, Python for Data Analysis - Pandas, Python for Data Visualization

Regular Expressions

1. RE Objects and Pattern matching
2. Parsing data
3. Subexpressions
4. Complex substitutions
5. RE tips and tricks

Highlights of the Standard Library

1. Lights of the Standard Library
2. Working with the operating system
3. Grabbing web pages
4. Sending email
5. Using glob for filename wildcards
6. Math and random
7. Accessing dates and times with datetime
8. Working with compressed files

Python Classes

1. About o-o programming
2. Defining classes
3. Constructors
4. Instance methods
5. Instance data
6. Class methods and data
7. Destructors

Python for Data Analysis - NumPy

1. Introduction
2. Ndarray Object
3. Data Types
4. Array Attributes
5. Array Creation Routines
6. Array from existing data
7. Numerical ranges
8. Array Indexing and Slicing
9. Advanced Indexing
10. Iterating over Array
11. Array Manipulation
12. Arithmetic Operators
13. Binary Operators
14. String Functions
15. Mathematical Functions
16. Statistical Functions

Python for Data Analysis – SciPy

1. Introduction
2. Basic functions
3. Special functions
4. Integration
5. Optimization
6. Interpolation
7. Fourier transforms
8. Signal Processing
9. Linear Algebra
10. Sparse Eigenvalue Problems with ARPACK
11. Compressed Sparse Graph Routines
12. Spatial data structures and algorithms
13. Statistics
14. Multidimensional image processing
15. File IO

Python for Data Analysis - Pandas

1. Introduction to Pandas
2. Series
3. DataFrames
4. Missing Data
5. Groupby
6. Merging Joining and Concatenating
7. Operations
8. Data Input and Output

Python for Data Visualization

1. Matplotlib
2. Seaborn
3. Distribution Plots
4. Categorical Plots
5. Matrix Plots
6. Grids
7. Regression Plots
8. Pandas Built-in Data Visualization
9. Plotly and Cufflinks
10. Geographical Plotting
11. Choropleth Maps

Day 4 - Machine Learning Concepts Overview, Linear Regression, Logistic Regression, K Nearest Neighbors, Decision Trees and Random Forests, Support Vector Machines

Machine Learning Concepts Overview

1. Introduction to Machine Learning
2. Why Machine Learning
3. Types of Machine Learning Algorithms
4. Supervised Machine Learning Process
5. Unsupervised Machine Learning Process
6. Evaluating Performance - Classification Error Metrics
7. Evaluating Performance - Regression Error Metrics
8. Machine Learning with Python

Linear Regression

1. Theory
2. Model Selection Updates for SciKit Learn 0.18
3. Linear Regression with Python
4. Linear Regression Project
5. Cross Validation and Bias-Variance Trade-Off
6. Bias Variance Trade-Off

Logistic Regression

1. Theory
2. Logistic Regression with Python
3. Logistic Regression Project

K Nearest Neighbors

1. KNN Theory
2. KNN with Python
3. KNN Project

Decision Trees and Random Forests

1. Introduction to Tree Methods
2. Decision Trees and Random Forest with Python
3. Decision Trees and Random Forest Project

Support Vector Machines

1. SVM Theory
2. Support Vector Machines with Python
3. SVM Project

Day 5 -K Means Clustering, Principal Component Analysis, Recommender Systems, Natural Language Processing, Neural Nets and Deep Learning,

K Means Clustering

1. K Means Algorithm Theory
2. K Means with Python
3. K Means Project

Principal Component Analysis

1. Introduction
2. PCA with Python

Recommender Systems

1.Introduction
2.Recommender Systems with Python - Part 1
3.Recommender Systems with Python - Part 2

Natural Language Processing

1.Natural Language Processing Theory
2.NLP with Python
3.NLP Project

Neural Nets and Deep Learning

1. Welcome to the Deep Learning Section!
2. Introduction to Artificial Neural Networks (ANN)
3. Installing TensorFlow
4. Perceptron Model
5. Neural Networks
6. Activation Functions
7. Multi-Class Classification Considerations
8. Cost Functions and Gradient Descent
9. Backpropagation
10. TensorFlow vs Keras
11. TF Syntax Basics - Part One - Preparing the Data
12. TF Syntax Basics - Part Two - Creating and Training the Model
13. TF Syntax Basics - Part Three - Model Evaluation
14. TF Regression Code Along - Exploratory Data Analysis
15. TF Regression Code Along - Exploratory Data Analysis - Continued
16. TF Regression Code Along - Data Pre-processing and Creating a Model
17. TF Regression Code Along - Model Evaluation and Predictions
18. TF Classification Code Along - EDA and Pre-processing
19. TF Classification - Dealing with Overfitting and Evaluation
20. TensorFlow 2.0 Project Options Overview
21. TensorFlow 2.0 Project Notebook Overview
22. Keras Project Solutions - Dealing with Missing Data
23. Keras Project Solutions - Categorical Data
24. Keras Project Solutions - Data Pre-processing
25. Keras Project Solutions - Creating and Training a Model
26. Keras Project Solutions - Model Evaluation
27. TensorBoard

Course Modules Covered in the Machine Learning Using Python program

Day 1 - An Overview of Python, Running Python Scripts, Getting Started, Flow Control, Sequence Data

An Overview of Python

1. What is Python?
2. Interpreted languages.
3. Advantages and disadvantages.
4. Downloading and installing.
5. Which version of Python.
6. Where to find documentation.

Running Python Scripts

1. Structure of a Python script
2. Using the interpreter interactively
3. Running standalone scripts under Unix and Windows

Getting Started

1. Using variables
2. String types: normal, raw and Unicode
3. String operators and expressions
4. Math operators and expressions
5. Writing to the screen
6. Command line parameters
7. Reading from the keyboard

Flow Control

1. About flow control
2. Indenting is significant
3. The if statements
4. The elif statements
5. While loops
6. Using lists
7. Using the for statement
8. The range() function

Sequence Data

1. list operations
2. list methods
3. Strings are special kinds of lists
4. Tuples
5. Sets
6. Dictionaries

Day 2 - Defining Functions, Working with Files, Dictionaries and Sets, Errors and Exception Handling, Using Modules

Defining Functions

1. Syntax of function definition
2. Formal parameters
3. Global versus local variables
4. Passing parameters and returning values

Working with Files

1. Text file I/O overview
2. Opening a text file
3. Reading text files
4. Raw (binary) data
5. Using the pickle module
6. Writing to a text file
7. Opening Excel File
8. Reading from Excel File
9. Writing data into Excel File

Dictionaries and Sets

1. Dictionary overview
2. Creating dictionaries
3. Dictionary functions
4. Fetching keys or values
5. Testing for existence of elements
6. Deleting elements

Errors and Exception Handling

1. Dealing with syntax errors
2. Exceptions
3. Handling exceptions with try/except
4. Cleaning up with finally

Using Modules

1. What is a module?
2. The import statement
3. Function aliases
4. Packages
5. Standard Modules – sys
6. Standard Modules – math
7. Standard Modules – time

Day 3 - Regular Expressions, Highlights of the Standard Library, Python Classes, Python for Data Analysis - NumPy, Python for Data Analysis – SciPy, Python for Data Analysis - Pandas, Python for Data Visualization

Regular Expressions

1. RE Objects and Pattern matching
2. Parsing data
3. Subexpressions
4. Complex substitutions
5. RE tips and tricks

Highlights of the Standard Library

1. Lights of the Standard Library
2. Working with the operating system
3. Grabbing web pages
4. Sending email
5. Using glob for filename wildcards
6. Math and random
7. Accessing dates and times with datetime
8. Working with compressed files

Python Classes

1. About o-o programming
2. Defining classes
3. Constructors
4. Instance methods
5. Instance data
6. Class methods and data
7. Destructors

Python for Data Analysis - NumPy

1. Introduction
2. Ndarray Object
3. Data Types
4. Array Attributes
5. Array Creation Routines
6. Array from existing data
7. Numerical ranges
8. Array Indexing and Slicing
9. Advanced Indexing
10. Iterating over Array
11. Array Manipulation
12. Arithmetic Operators
13. Binary Operators
14. String Functions
15. Mathematical Functions
16. Statistical Functions

Python for Data Analysis – SciPy

1. Introduction
2. Basic functions
3. Special functions
4. Integration
5. Optimization
6. Interpolation
7. Fourier transforms
8. Signal Processing
9. Linear Algebra
10. Sparse Eigenvalue Problems with ARPACK
11. Compressed Sparse Graph Routines
12. Spatial data structures and algorithms
13. Statistics
14. Multidimensional image processing
15. File IO

Python for Data Analysis - Pandas

1. Introduction to Pandas
2. Series
3. DataFrames
4. Missing Data
5. Groupby
6. Merging Joining and Concatenating
7. Operations
8. Data Input and Output

Python for Data Visualization

1. Matplotlib
2. Seaborn
3. Distribution Plots
4. Categorical Plots
5. Matrix Plots
6. Grids
7. Regression Plots
8. Pandas Built-in Data Visualization
9. Plotly and Cufflinks
10. Geographical Plotting
11. Choropleth Maps

Day 4 - Machine Learning Concepts Overview, Linear Regression, Logistic Regression, K Nearest Neighbors, Decision Trees and Random Forests, Support Vector Machines

Machine Learning Concepts Overview

1. Introduction to Machine Learning
2. Why Machine Learning
3. Types of Machine Learning Algorithms
4. Supervised Machine Learning Process
5. Unsupervised Machine Learning Process
6. Evaluating Performance - Classification Error Metrics
7. Evaluating Performance - Regression Error Metrics
8. Machine Learning with Python

Linear Regression

1. Theory
2. Model Selection Updates for SciKit Learn 0.18
3. Linear Regression with Python
4. Linear Regression Project
5. Cross Validation and Bias-Variance Trade-Off
6. Bias Variance Trade-Off

Logistic Regression

1. Theory
2. Logistic Regression with Python
3. Logistic Regression Project

K Nearest Neighbors

1. KNN Theory
2. KNN with Python
3. KNN Project

Decision Trees and Random Forests

1. Introduction to Tree Methods
2. Decision Trees and Random Forest with Python
3. Decision Trees and Random Forest Project

Support Vector Machines

1. SVM Theory
2. Support Vector Machines with Python
3. SVM Project

Day 5 -K Means Clustering, Principal Component Analysis, Recommender Systems, Natural Language Processing, Neural Nets and Deep Learning,

K Means Clustering

1. K Means Algorithm Theory
2. K Means with Python
3. K Means Project

Principal Component Analysis

1. Introduction
2. PCA with Python

Recommender Systems

1.Introduction
2.Recommender Systems with Python - Part 1
3.Recommender Systems with Python - Part 2

Natural Language Processing

1.Natural Language Processing Theory
2.NLP with Python
3.NLP Project

Neural Nets and Deep Learning

1. Welcome to the Deep Learning Section!
2. Introduction to Artificial Neural Networks (ANN)
3. Installing TensorFlow
4. Perceptron Model
5. Neural Networks
6. Activation Functions
7. Multi-Class Classification Considerations
8. Cost Functions and Gradient Descent
9. Backpropagation
10. TensorFlow vs Keras
11. TF Syntax Basics - Part One - Preparing the Data
12. TF Syntax Basics - Part Two - Creating and Training the Model
13. TF Syntax Basics - Part Three - Model Evaluation
14. TF Regression Code Along - Exploratory Data Analysis
15. TF Regression Code Along - Exploratory Data Analysis - Continued
16. TF Regression Code Along - Data Pre-processing and Creating a Model
17. TF Regression Code Along - Model Evaluation and Predictions
18. TF Classification Code Along - EDA and Pre-processing
19. TF Classification - Dealing with Overfitting and Evaluation
20. TensorFlow 2.0 Project Options Overview
21. TensorFlow 2.0 Project Notebook Overview
22. Keras Project Solutions - Dealing with Missing Data
23. Keras Project Solutions - Categorical Data
24. Keras Project Solutions - Data Pre-processing
25. Keras Project Solutions - Creating and Training a Model
26. Keras Project Solutions - Model Evaluation
27. TensorBoard

Key Features

40 hours of instructor led training
Fully Online
Class recording available
Interactive Learning
3 month Coaching Session
100% HRDF SBL-KHAS Claimable!

Pre-Requisites

Basic Programming Knowledge
Analytical Mindset
Willingness to self learn online
No prior experience is required
We will start from the very basics
Committed to complete all tasks

Who Should Join

Professional switching careers
Business Analysts
IT Engineers
Students
New Programmers
Anyone interested in Python

Program Key Highlights

40 hours of Remote Online Learning

80 Additional Self Learning Hours

12 Live Hands-on Projects

Certified by International Body

Mentorship with Industry Experts

Designed for Beginners & Professionals

Request for more Information

Get Professionally Certified

Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).

This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.

Be a part of the global network of data science professionals and join the community across sectors.

Why Online Bootcamp

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn by working on real-world problems

Capstone projects involving real world data sets with virtual labs for hands-on learning

Structured guidance ensuring learning never stops

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts.

Machine Learning Training FAQs

1Do I Get A Certificate At The End Of The Course?

Yes.You will get a certificate at the end of the course from Thulija Academy.

2How Will The Labs Be Conducted?

Labs will be conducted through online means. Recorded playbacks are provided to participants after the sessions are over.

Bootcamp Fee

RM2,300

One-time fee. Lifetime access to next bootcamp.

Only collected if you’re accepted.

What’s Included

Starter kit sent to you.

30-days of Bootcamp content.

8 live online training sessions.

Downloads, recording access and extra content.

Lifetime student support.

Demo-day & certification.

When & Where

Bootcamp starts on: 12th July 2021

Venue: Digital Classroom. You can participate from anywhere in Malaysia – as long as you have an internet connection.

Join Us For Our Next Program

And step up your Machine Learning Career

*Payments are only accepted once applicants are enrolled into the program.*