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 CodeML104
- AudienceAnyone with interest in Machine Learning And Python
- LanguageEnglish
- CertificationParticipants will receive a certificate of competence.

Tools Covered For This Course:

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

- What is Python?
- Interpreted languages
- Advantages and disadvantages
- Downloading and installing
- Which version of Python
- Where to find documentation

#### Running Python Scripts

- Structure of a Python script
- Using the interpreter interactively
- Running standalone scripts under Unix and Windows

#### Getting Started

- Using variables
- String types: normal, raw and Unicode
- String operators and expressions
- Math operators and expressions
- Writing to the screen
- Command line parameters
- Reading from the keyboard

#### Flow Control

- About flow control
- Indenting is significant
- The if statements
- The elif statements
- while loops
- Using lists
- Using the for statement
- The range() functionm

#### Sequence Data

- list operations
- list methods
- Strings are special kinds of lists
- tuples
- sets
- Dictionaries

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

#### Defining Functions

- Syntax of function definition
- Formal parameters
- Global versus local variables
- Passing parameters and returning values

#### Working with Files

- Text file I/O overview
- Opening a text file
- Reading text files
- Raw (binary) data
- Using the pickle module
- Writing to a text file
- Opening Excel File
- Reading from Excel File
- Writing data into Excel File

#### Dictionaries and Sets

- Dictionary overview
- Creating dictionaries
- Dictionary functions
- Fetching keys or values
- Testing for existence of elements
- Deleting elements

#### Errors and Exception Handling

- Dealing with syntax errors
- Exceptions
- Handling exceptions with try/except
- Cleaning up with finally

#### Using Modules

- What is a module?
- The import statement
- Function aliases
- Packages
- Standard Modules – sys
- Standard Modules – math
- 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

- RE Objects and Pattern matching
- Parsing data
- Subexpressions
- Complex substitutions
- RE tips and tricks

#### Highlights of the Standard Library

- Lights of the Standard Library
- Working with the operating system
- Grabbing web pages
- Sending email
- Using glob for filename wildcards
- Math and random
- Accessing dates and times with datetime
- Working with compressed files

#### Python Classes

- About o-o programming
- Defining classes
- Constructors
- Instance methods
- Instance data
- Class methods and data
- Destructors

#### Python for Data Analysis - NumPy

- Introduction
- Ndarray Object
- Data Types
- Array Attributes
- Array Creation Routines
- Array from existing data
- Numerical ranges
- Array Indexing and Slicing
- Advanced Indexing
- Iterating over Array
- Array Manipulation
- Arithmetic Operators
- Binary Operators
- String Functions
- Mathematical Functions
- Statistical Functions

#### Python for Data Analysis – SciPy

- Introduction
- Basic functions
- Special functions
- Integration
- Optimization
- Interpolation
- Fourier transforms
- Signal Processing
- Linear Algebra
- Sparse Eigenvalue Problems with ARPACK
- Compressed Sparse Graph Routines
- Spatial data structures and algorithms
- Statistics
- Multidimensional image processing
- File IO

#### Python for Data Analysis - Pandas

- Introduction to Pandas
- Series
- DataFrames
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Operations
- Data Input and Output

#### Python for Data Visualization

- Matplotlib
- Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Pandas Built-in Data Visualization
- Plotly and Cufflinks
- Geographical Plotting
- 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

- Introduction to Machine Learning
- Why Machine Learning
- Types of Machine Learning Algorithms
- Supervised Machine Learning Process
- Unsupervised Machine Learning Process
- Evaluating Performance - Classification Error Metrics
- Evaluating Performance - Regression Error Metrics
- Machine Learning with Python

#### Linear Regression

- Theory
- Model Selection Updates for SciKit Learn 0.18
- Linear Regression with Python
- Linear Regression Project
- Cross Validation and Bias-Variance Trade-Off
- Bias Variance Trade-Off

#### Logistic Regression

- Theory
- Logistic Regression with Python
- Logistic Regression Project

#### K Nearest Neighbors

- KNN Theory
- KNN with Python
- KNN Project

#### Decision Trees and Random Forests

- Introduction to Tree Methods
- Decision Trees and Random Forest with Python
- Decision Trees and Random Forest Project

#### Support Vector Machines

- SVM Theory
- Support Vector Machines with Python
- SVM Project

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

#### K Means Clustering

- K Means Algorithm Theory
- K Means with Python
- K Means Project

#### Principal Component Analysis

- Introduction
- PCA with Python

#### Recommender Systems

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

#### Natural Language Processing

- Natural Language Processing Theory
- NLP with Python
- NLP Project

#### Neural Nets and Deep Learning

- Welcome to the Deep Learning Section!
- Introduction to Artificial Neural Networks (ANN)
- Installing TensorFlow
- Perceptron Model
- Neural Networks
- Activation Functions
- Multi-Class Classification Considerations
- Cost Functions and Gradient Descent
- Backpropagation
- TensorFlow vs Keras
- TF Syntax Basics - Part One - Preparing the Data
- TF Syntax Basics - Part Two - Creating and Training the Model
- TF Syntax Basics - Part Three - Model Evaluation
- TF Regression Code Along - Exploratory Data Analysis
- TF Regression Code Along - Exploratory Data Analysis - Continued
- TF Regression Code Along - Data Pre-processing and Creating a Model
- TF Regression Code Along - Model Evaluation and Predictions
- TF Classification Code Along - EDA and Pre-processing
- TF Classification - Dealing with Overfitting and Evaluation
- TensorFlow 2.0 Project Options Overview
- TensorFlow 2.0 Project Notebook Overview
- Keras Project Solutions - Dealing with Missing Data
- Keras Project Solutions - Categorical Data
- Keras Project Solutions - Data Pre-processing
- Keras Project Solutions - Creating and Training a Model
- Keras Project Solutions - Model Evaluation
- 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.

Join Us For Our Next Program

And step up your Machine Learning Career

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

Contact us on Whatsapp for more enquiries