Professional Certification
In Machine Learning
 
Become a Machine Learning specialist,
gain holistic knowledge in ML algorithms.
 
6 Days / 48 Hours / 28 Modules
+ 3 month coaching + 1 year eLearning access
 
funding-schemes
Professional
Certification in
Machine Learning
Become a Machine
Learning specialist,
gain holistic knowledge
in ML algorithms.
6 Days / 48 Hours / 28 Modules
+ 3 month coaching + 1 year eLearning access
sbl-khas-white text
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

    Participants will receive a certificate of competence.
 
Tools Covered For This Course:
python
numpy
scipy
pandas
keras-logo
tensor-logo
 
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 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 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() functionm

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
online-learning-2
40 hours of Remote Online Learning
learning-hours
80 Additional Self Learning Hours
hands-on
12 Live Hands-on Projects
certification
Certified by International Body
mentor
Mentorship with Industry Experts
industry
Designed for Beginners & Professionals
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