Machine Learning Masters Program

Read Review
5.0 (3000 satisfied learners)

Enrol now to become a Certified Machine Learning expert with CertZip Machine Learning Masters Program and upgrade your skills.

Course Description

This Machine Learning Program makes you trained in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. Our Machine learning course contains training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning, such as Deep Learning, Graphical Models and Reinforcement Learning.

A master's in Machine Learning (ML) coursework explores the fundamental mathematics of artificial intelligence and machine learning while enabling students to develop related tools and apply AI and ML to various real-world problems.

Our Machine Learning Course Learning track has been curated after thorough research and recommendations from industry experts. It will help you differentiate yourself with multi-platform fluency and have real-world experience with the essential tools and platforms.

There are no prerequisites for enrolment in the Machine learning Masters Program.

Experienced professional working in the IT industry. An aspirant is planning to enter the data-driven world of Machine Learning.

A machine learning engineer is an engineer that utilizes programming languages such as Python, Java, Scala, etc., to run experiments with the correct machine learning libraries.

As a machine learning engineer working in this branch of artificial intelligence, you'll be accountable for developing programmes and algorithms that allow machines to take steps without being directed.

Machine-learning jobs have jumped by almost 75 per cent over the past four years and are poised to keep growing. Pursuing a machine learning position is a solid choice for a high-paying profession that will be in demand for decades.

What you'll learn

  • In this course, you will learn: Supervised Learning, Unsupervised Learning Natural Language Processing and more.


  • There are requirements for learning this course.


understand the fundamental concepts of Python.

Need for Programming,
Advantages of Programming,
Overview of Python,
Organizations using Python,
Python Applications in Various Domains,
Python Installation,
Operands and Expressions,
Conditional Statements,
Command Line Arguments

learn various types of sequence structures, their use, and perform sequence operations.

Method of Accepting User Input and eval Function
Python - Files Input/Output Functions,
Lists and Related Operations,
Tuples and Related Operations,
Strings and Related Operations,
Sets and Related Operations,
Dictionaries and Related Operations

learn about different types of Functions and various Object-Oriented concepts such as Abstraction, Inheritance, Polymorphism, Overloading, Constructor, and so on.

User-Defined Functions,
Concept of Return Statement,
Concept of __name__=” __main__”,
Function Parameters,
Different Types of Arguments,
Global Variables,
Global Keyword,
Variable Scope and Returning Values,
Lambda Functions,
Various Built-In Functions,
Introduction to Object-Oriented Concepts,
Built-In Class Attributes,
Public, Protected, and Private Attributes, and Methods,
Class Variable and Instance Variable,
Constructor and Destructor,
Decorator in Python,
Core Object-Oriented Principles,
Inheritance and Its Types,
Method Resolution Order,
Getter and Setter Methods,
Inheritance-In-Class Case Study

Discover how to make generic python scripts, address errors/exceptions in code, and extract/filter content using regex.

Standard Libraries,
Packages and Import Statements,
Reload Function,
Important Modules in Python,
Sys Module,
Os Module,
Math Module,
Date-Time Module,
Random Module,
JSON Module,
Regular Expression,
Exception Handling

basics of Data Analysis utilizing two essential libraries: NumPy and Pandas, the concept of file handling using the NumPy library.

Basics of Data Analysis,
NumPy - Arrays,
Operations on Arrays,
Indexing Slicing and Iterating,
NumPy Array Attributes,
Matrix Product,
NumPy Functions,
Array Manipulation,
File Handling Using NumPy

gain in-depth knowledge about exploring datasets and data manipulation utilizing Pandas.

Introduction to pandas,
Data structures in pandas,
Series, Data Frames,
Importing and Exporting Files in Python,
Basic Functionalities of a Data Object,
Merging of Data Objects,
Concatenation of Data Objects,
Types of Joins on Data Objects,
Data Cleaning using pandas,
Exploring Datasets

you will learn Data Visualization using Matplotlib.

Why Data Visualization?
Matplotlib Library,
Line Plots,
Multiline Plots,
Bar Plot,
Pie Chart,
Scatter Plot,
Saving Charts,
Customizing Visualizations,
Saving Plots,

you will learn GUI programming using the ipywidgets package.

Ipywidgets Package,
Numeric Widgets,
Boolean Widgets,
Selection Widgets,
String Widgets,
Date Picker,
Color Picker,
Container Widgets,
Creating a GUI Application

you will get to learn to design Python Applications.

Use of Folium Library,
Use of Pandas Library,
Flow Chart of Web Map Application,
Developing Web Map Using Folium and Pandas,
Reading Information from Titanic Dataset and Represent It Operating Plots.

you will learn to design Python Applications.

Beautiful Soup Library,
Requests Library,
Scrap All Hyperlinks from a Webpage UtilizingUtilizing Beautiful Soup and Requests,
Plotting Charts Using Bokeh,
Plotting Scatterplots Using Bokeh,
Image Editing Using OpenCV,
Face Detection Using OpenCV,
Motion Detection and Capturing Video

Understand Machine learning with Python training and see how Data Science helps analyze large and unstructured data with various tools.

What is Data Science?
What does Data Science involve?
The era of Data Science
Business Intelligence vs Data Science
The life cycle of Data Science
Tools of Data Science
Introduction to Python

structured form, analyzing the data, and representing the data in a graphical format.

Data Analysis Pipeline,
What is Data Extraction,
Types of Data,
Raw and Processed Data,
Data Wrangling,
Exploratory Data Analysis,
Visualization of Data

you will learn the concept of Machine Learning with Python and its types.

Python Revision (NumPy, Pandas, sci-kit know, matplotlib)
What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Gradient descent

know Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
Implementation of Logistic regression, Decision tree, Random forest

learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress sizes. Also, you will be developing an LDA model.

Introduction to Dimensionality,
Why Dimensionality Reduction,
Factor Analysis,
Scaling dimensional model,

learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

What is Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier
What is a Support Vector Machine?
Illustrate how Support Vector Machine works?
Hyperparameter optimization optimization
Grid Search vs Random Search
Implementation of Support Vector Machine for Classification
Implementation of Naïve Bayes, SVM

learns about Unsupervised Learning and the various types of clustering used to analyze and analyze the data.

What is Clustering & its Use Cases?
What is K-means Clustering?
How does the K-means algorithm work?
How to do optimal clustering
What is C-means Clustering?
What is Hierarchical Clustering?
How does Hierarchical Clustering work?
Implementing K-means Clustering
Implementing Hierarchical Clustering

understand Association rules and their extension towards recommendation engines with the Apriori algorithm.

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How do Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering
Apriori Algorithm
Market Basket Analysis

learn about developing an intelligent learning algorithm such that the Learning becomes more and more accurate as time passes.

What is Reinforcement Learning,
Why Reinforcement Learning,
Elements of Reinforcement Learning,
Exploration vs Exploitation dilemma,
Epsilon Greedy Algorithm,
Markov Decision Process (MDP),
Q values and V values,
Q – Learning,
α values,
Calculating Reward,
Discounted Reward,
Calculating Optimal quantities,
Implementing Q Learning,
Setting up an Optimal Action

know about Time Series Analysis to predict dependent variables based on time. You will be taught different models for time series modelling such that you analyze accurate time-dependent data for forecasting.

What is Time Series Analysis?
Importance of TSA,