Machine Learning Masters Program

Categories
AI.
Read Review
5.0 (3000 satisfied learners)

Enrol now to become a Certified Machine Learning expert with EDTIA 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.

Requirements

  • There are requirements for learning this course.

Curriculam

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,
Variables,
Operands and Expressions,
Conditional Statements,
Loops,
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,
Overloading,
Overriding,
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,
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,
Histogram,
Pie Chart,
Scatter Plot,
Boxplot,
Saving Charts,
Customizing Visualizations,
Saving Plots,
Grids,
Subplots

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,
PCA,
Factor Analysis,
Scaling dimensional model,
LDA,
PCA,
Scaling

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,
Components of TSA,
White Noise,
AR model,
MA model,
ARMA model,
ARIMA model,
Stationarity,
ACF & PACF,
Checking Stationarity,
Converting a non-stationary data to stationary,
Implementing Dickey-Fuller Test,
Plot ACF and PACF,
Generating the ARIMA plot,
TSA Forecasting

learn about selecting one model over another. You will understand how to transform weaker algorithms into stronger ones.

What is Model Selection?
Need for Model Selection,
Cross-Validation,
What is Boosting?
How do Boosting Algorithms work?
Types of Boosting Algorithms,
Adaptive Boosting,
Cross-Validation,
AdaBoost

know how to approach and execute a Project end to end, we will be having a Q&A and doubt clearing session.

At the end of this machine learning training section, you should be capable to: How to approach a project, Hands-On project implementation, What Industry experts, Industry insights for the Machine Learning domain, QA and Doubt Clearing Session

learn about text mining and how to extract and read data from common file types, including NLTK corpora.

Overview of Text Mining,
Need for Text Mining,
Natural Language Processing (NLP) in Text Mining,
Applications of Text Mining,
OS Module,
Reading, Writing to text and word files,
Setting the NLTK Environment,
Accessing the NLTK Corpora,
Install NLTK Packages using NLTK Downloader,
Accessing your operating system utilizing the OS Module in Python,
Reading & Writing .txt Files from/to your Local,
Reading & Writing .docx Files from/to your Local,
Working with the NLTK Corpora

comprehend some ways of text extraction and Cleaning utilizing NLTK.

Tokenization,
Frequency Distribution,
Different Types of Tokenizers,
Bigrams, Trigrams & Ngrams,
Stemming,
Lemmatization,
Stopwords,
POS Tagging,
Named Entity Recognition,
Tokenization: Regex, Word, Blank line, Sentence Tokenizers,
Bigrams, Trigrams & Ngrams,
Stopword Removal
POS Tagging
Named Entity Recognition (NER)

learn how to analyze and analyze a sentence structure using a group of words to create phrases and sentences using NLP and English grammar rules.

Syntax Trees,
Chunking,
Chinking,
Context-Free Grammars (CFG),
Automating Text Paraphrasing,
Parsing Syntax Trees,
Chunking,
Chinking,
Automate Text Paraphrasing using CFG's

explore text classification, vectorization techniques and processing utilizing scikit-learn

Machine Learning: Brush Up,
Bag of Words,
Count Vectorizer,
Term Frequency (TF),
Inverse Document Frequency (IDF),
Demonstrate Bag of Words Approach,
Working with CountVectorizer(),
Using TF & IDF

comprehend to create a Machine Learning classifier for text classification

Converting text to features and labels,
Multinomial Naive Bayes Classifier,
Leveraging Confusion Matrix,
Converting text to features and labels,
Demonstrate text classification using Multinomial NB Classifier,
Leveraging Confusion Matrix,

learn Sentiment Classification on Movie Rating Dataset

Objective: At the end of this module, you should be capable to: Execute all the text processing techniques starting with tokenization tokenization, Communicate your future to end work on Text Mining, Execute Machine Learning along with Text Processing, Sentiment Analysis

understand the concepts of Deep Learning and learn how it differs from machine learning. This Deep Learning Certification module will also brief you on implementing the single-layer perceptron concept.

What is Deep Learning?
Curse of Dimensionality,
Machine Learning vs Deep Learning,
Use cases of Deep Learning,
Human Brain vs Neural Network,
What is Perceptron?
Learning Rate ,
Epoch,
Batch Size,
Activation Function,
Single Layer Perceptron

Learn TensorFlow 2. x. You will install and validate TensorFlow 2. x by building a Simple Neural Network to predict handwritten digits and using Multi-Layer Perceptron to improvise the model's accuracy.

Introduction to TensorFlow 2. x
Installing TensorFlow 2. x
Defining Sequence model layers
Activation Function
Layer Types
Model Compilation
Model Optimizer
Model Loss Function
Model Training
Digit Classification utilizing Simple Neural Network in TensorFlow 2. x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer

comprehend how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by studying the theory behind how CNN is used to predict 'X' or 'O'. You will also use CNN VGG-16 utilizing TensorFlow 2 and indicate whether the given image is of a 'cat' or a 'dog' and save and load a model's weight.

Image Classification Example,
What is Convolution,
Convolutional Layer Network,
Convolutional Layer,
Filtering,
ReLU Layer,
Pooling,
Data Flattening,
Fully Connected Layer,
Predicting a cat or a dog,
Saving and Loading a Model,
Face Detection using OpenCV

understand the concept and working of RCNN and figure out why it was developed in the first place.

Regional-CNN,
Selective Search Algorithm,
Bounding Box Regression,
SVM in RCNN,
Pre-trained Model,
Model Accuracy,
Model Inference Time,
Model Size Comparison,
Transfer Learning,
Object Detection – Evaluation,
mAP,
IoU,
RCNN – Speed Bottleneck,
Fast R-CNN,
RoI Pooling,
Fast R-CNN – Speed Bottleneck,
Faster R-CNN,
Feature Pyramid Network (FPN),
Regional Proposal Network (RPN),
Mask R-CNN

Understand what a Boltzmann Machine is and its implementation, learn what an Autoencoder is, its various types, and how it works.

What is Boltzmann Machine (BM)?
Identify the issues with BM
Why did RBM come into the picture?
Step by step implementation of RBM
Distribution of Boltzmann Machine
Understanding Autoencoders
Architecture of Autoencoders
Brief on types of Autoencoders