Machine Learning Masters Program

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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

Applications of Autoencoders

understand the negative generative model and how it works by implementing a step-by-step Generative Adversarial Network.

Which Face is Fake?

Understanding GAN,

What is Generative Adversarial Network?

How does GAN work?

Step by step Generative Adversarial Network implementation,

Types of GAN,

Recent Advances: GAN

classify each emotion shown in the facial expression into various categories by creating a CNN model to identify the images' facial expressions and anticipate the uploaded image's facial expression. Use OpenCV and Haar Cascade File during the project implementation to check the emotion.

Where do we use Emotion and Gender Detection?

How does it work?

Emotion Detection architecture

Face/Emotion detection using Haar Cascades

Implementation on Colab

differentiate between Feed Forward Network and Recurrent neural network (RNN) and understand how RNN works, understand and learn about GRU and implement Sentiment Analysis utilizing RNN and GRU.

Issues with Feed Forward Network,

Recurrent Neural Network (RNN),

Architecture of RNN,

Calculation in RNN,

Backpropagation and Loss calculation,

Applications of RNN,

Vanishing Gradient,

Exploding Gradient,

What is GRU?

Components of GRU,

Update gate,

Reset gate,

Current memory content,

Final memory at current time step

understand the architecture of LSTM and the significance of gates in LSTM. Differentiate between sequence-based models and improve the model's efficiency utilizing BPTT.

What is LSTM?

Structure of LSTM,

Forget Gate,

Input Gate,

Output Gate,

LSTM architecture,

Types of Sequence-Based Model,

Sequence Prediction,

Sequence Classification,

Sequence Generation,

Types of LSTM,

Vanilla LSTM,

Stacked LSTM,

CNN LSTM,

Bidirectional LSTM,

How to increase the efficiency of the model?

Backpropagation through time,

Workflow of BPTT

execute Auto Image captioning utilizing pre-trained model Inception V3 and LSTM for text processing.

Auto Image Captioning,

COCO dataset,

Pre-trained model,

Inception V3 model,

The architecture of Inception V3,

Modify the last layer of a pre-trained model,

Freeze model,

CNN for image processing,

LSTM or text processing

understand Big Data, the limitations of the existing solutions for Big Data problems, how Hadoop solves the Big Data problem, Hadoop ecosystem components, Hadoop Architecture, HDFS, Rack Awareness, and Replication. You will learn about the Hadoop Cluster Architecture and important configuration files in a Hadoop Cluster. You will also get an introduction to Spark, why it is used and an understanding of the difference between batch processing and real-time processing.

What is Big Data?

Big Data Customer Scenarios,

Regulations and Resolutions of Existing Data Analytics Architecture with Uber Use Case,

How does Hadoop Solve the Big Data Problem?

What is Hadoop?

Hadoop's Key Characteristics,

Hadoop Ecosystem and HDFS,

Hadoop Core Components,

Rack Awareness and Block Replication,

YARN and its Advantage,

Hadoop Cluster and its Architecture,

Hadoop: Different Cluster Modes,

Big Data Analytics with Batch & Real-Time Processing,

Why is Spark Needed?

What is Spark?

How does Spark Differ from its Competitors?

Spark at eBay,

Spark's Place in Hadoop Ecosystem

learn Python programming basics and understand different types of sequence structures, related operations, and their usage. You will also learn diverse ways of opening, reading, and writing files.

Overview of Python,

Different Applications where Python is Used,

Values, Types, Variables,

Operands and Expressions,

Conditional Statements,

Loops,

Command Line Arguments,

Writing to the Screen,

Python files I/O Functions,

Numbers,

Strings and related operations,

Tuples and related operations,

Lists and related operations,

Dictionaries and related operations,

Sets and related operations,

Creating "Hello World" code,

Demonstrating Conditional Statements,

Demonstrating Loops,

Tuple - properties, associated processes, compared with the list,

List - properties, related operations,

Dictionary - properties, related operations,

Set - properties, related operations

know how to make generic python scripts, address errors/exceptions in code and finally, how to extract/filter content utilizing regex.

Functions,

Function Parameters,

Global Variables,

Variable Scope and Returning Values,

Lambda Functions,

Object-Oriented Concepts,

Standard Libraries,

Modules Used in Python,

The Import Statements,

Module Search Path,

Package Installation Ways,

Functions - Syntax, Arguments, Keyword Arguments, Return Values,

Lambda - Features, Syntax, Options, Compared with the Functions,

Sorting - Sequences, Dictionaries, Limitations of Sorting,

Errors and Exceptions - Types of Issues, Remediation,

Packages and Module - Modules, Import Options, sys Path

understand Apache Spark in-depth, and you will be learning about various Spark components; you will be creating and running multiple spark applications. In the end, you will learn how to perform data ingestion using Sqoop.

Spark Components & its Architecture,

Spark Deployment Modes,

Introduction to PySpark Shell,

Submitting PySpark Job,

Spark Web UI,

Writing PySpark Job Utilizing Jupyter Notebook,

Data Ingestion using Sqoop,

Building and Running Spark Application,

Spark Application Web UI,

Understanding different Spark Properties,

know about Spark - RDDs and other RDD related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).

Challenges in Existing Computing Methods

Probable Resolution & How RDD Decodes the Problem

What is RDD, Its Operations, Transformations & Actions

Data Loading and Saving Through RDDs,

Key-Value Pair RDDs,

Other Pair RDDs,

Two Pair RDDs,

RDD Lineage,

RDD Persistence,

WordCount Program Using RDD Concepts,

RDD Partitioning & How it Helps Achieve ParallelizationParallelization

Passing Functions to Spark,

Loading data in RDDs,

Saving data through RDDs,

RDD Transformations,

RDD Actions and Functions,

RDD Partitions,

WordCount through RDDs

understand SparkSQL, which is utilized to process structured data with SQL questions. You will learn about data-frames and datasets in Spark SQL, and different SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.

Need for Spark SQL,

What is Spark SQL,

Spark SQL Architecture,

SQL Context in Spark SQL,

Schema RDDs,

User-Defined Functions,

Data Frames & Datasets,

Interoperating with RDDs,

JSON and Parquet File Formats,

Loading Data through Different Sources,

Spark-Hive Integration,

Spark SQL – Creating data frames,

Loading and transforming data through different sources,

Stock Market Analysis,

Spark-Hive Integration

understand why machine learning is required, various Machine Learning techniques/algorithms and their execution utilizing Spark MLlib.

Why Machine Learning,

What is Machine Learning,

Where Machine Learning is used,

Face Detection: USE CASE,

Different Types of Machine Learning Techniques,

Introduction to MLlib,

Features of MLlib and MLlib Tools,

Various ML algorithms supported by MLlib

you will be implementing various algorithms supported by MLlib, such as Linear Regression, Decision Tree, Random Forest, etc.

Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest

Unsupervised Learning: K-Means Clustering & How It Works with MLlib

Examination of US Election Data utilizing MLlib (K-Means)

K- Means Clustering

Linear Regression

Logistic Regression

Decision Tree

Random Forest

understand Kafka and Kafka Architecture. Afterwards, you will go through the details of the Kafka Cluster, and you will also learn how to configure different types of Kafka Cluster. You will also introduce Apache Flume, its basic architecture, and its integration with Apache Kafka for event processing.

Need for Kafka,

What is Kafka,

Core Concepts of Kafka,

Kafka Architecture,

Where is Kafka Used,

Understanding the Components of the Kafka Cluster,

Configuring Kafka Cluster,

Kafka Producer and Consumer Java API,

Need for Apache Flume,

What is Apache Flume,

Basic Flume Architecture,

Flume Sources,

Flume Sinks,

Flume Channels,

Flume Configuration,

Integrating Apache Flume and Apache Kafka,

Configuring Single Node Single Broker Cluster,

Configuring Single Node Multi-Broker Cluster,

Creating and consuming messages via Kafka Java API,

Flume Commands,

Setting up Flume Agent,

Streaming Twitter Data into HDFS

work on Spark streaming to build scalable fault-tolerant streaming applications. Learn about DStreams and different Transformations performed on the streaming data.

Drawbacks in Existing Computing Methods,

Why Streaming is Necessary,

What is Spark Streaming,

Spark Streaming Features,

Spark Streaming Workflow,

How Uber Uses Streaming Data,

Streaming Context & DStreams,

Transformations on DStreams,

Describe Windowed Operators and Why it is Valid,

Important Windowed Operators,

Slice, Window and ReduceByWindow Operators,,

Stateful Operators,

WordCount Program using Spark Streaming

know about the various streaming data sources such as Kafka and flume. You will be able to create a spark streaming application.

Apache Spark Streaming: Data Sources,

Streaming Data Source Overview,

Apache Flume and Apache Kafka Data Sources,

Example: Using a Kafka Direct Data Source,

Various Spark Streaming Data Sources

Statement: A financial bank is trying to enlarge the financial inclusion of the unbanked population by providing a positive and safe borrowing experience. To ensure this underserved population has a favourable loan experience, it uses various alternative data--including telco and transactional information--to predict its clients' repayment abilities. The bank asked you to create a solution to guarantee that clients capable of repayment are accepted and that loans are given with a principal, maturity, and repayment calendar to empower their clients to succeed.

Statement: AnalyzeAnalyze and deduce the best performing movies based on customer feedback and review. Use two separate APIs (Spark RDD and Spark DataFrame) on datasets to find the best ranking movies.

learn the critical concepts of Spark GraphX programming and operations and different GraphX algorithms and their implementations.

Introduction to Spark GraphX,

Information about a Graph,

GraphX Basic APIs and Operations,

Spark GraphX Algorithm - PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation,

The Traveling Salesman problem,

Minimum Spanning Trees

FAQ

The average salary for a Machine Learning Engineer is $145,297

Quickly identifies trends and patterns. No human intervention is needed (automation) Continuous Improvement. Handling multi-dimensional and multi-variety data. Wide Applications.

Yes, if you're looking to follow a career in machine learning, a little coding is necessary.

To better understand the Machine Learning Masters Program Certification Training, one must learn as per the curriculum.

Learning how to use machine learning isn't harder than learning any other set of libraries for a programmer. If you require to sort data, you don't create a sort algorithm; you pick an appropriate algorithm and utilize it correctly.

Machine learning is essential because it gives enterprises a view of trends in customer behaviour and operational business patterns and supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a significant part of their operations.

Algorithms (searching, sorting, optimization, dynamic programming) Computability and complexity (P vs NP, NP-complete problems, big-O notation, approximate algorithms, etc.) Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)

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Machine Learning Masters Program

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Course Description

Description

What is the Machine Learning Masters Program Certification Training?

Why should you do Machine Learning Masters Program Certification Training?

What is the prerequisite for Machine Learning Masters Program Certification Training?

Who can do a Machine Learning Masters Program Certification Training?

What job is a Machine Learning Engineer?

What does a Machine Learning engineer do?

Are Machine Learning jobs in demand?

What you'll learn

Requirements

Curriculam

introduction to online live python training

sequences and file operations

Deep dives-functions of OOPs

Working with modules and handling expectations

Introduction to Numpy

Data manipulation using pandas

Data visualization using matplotlib

GUI programming

Developing web maps and representing information using plots (self-placed)

Computer vision using OpenCV visualization using bokeh (self-placed)

introduction to data science

Data extraction, wrangling & visualization, arranging the data in a

introduction to machine learning with Python

supervised learning - 1

Dimensionality Reduction

Supervied Learning - 2

Unsupervised Learning

Association rules Mining and Recommendation System

Reinforcement Learning

Time Series Analysis

Model Selection and Boosting

Project

Intro to text mining and NLP

Extracting, Cleaning and pre-processing text

Analyzing Sentence Structure

Text Classification 1

Text Classification 2

Project

Intro to Deep Learning

Getting Started with Tensor Flow 2.0

Convolution Neural Network

Regional CNN

Boltzmann Machine and Autoencoder

generative adversarial network (GAN)

Emotion and Gender Detection

Introduction to RNN and GNN

LSTM

Auto image captioning using CNN LSTM

Intro to Bigdata Hadoop and Spark

Introduction to Python for Apache Spark

Functions, OOPs and Module in Python

Deep Dive into Apache framework

Playing with spark RDDs

DataFrames and Spark SQL

Machine Learning using Spark MLib

Deep Dive into Spark MLlib

Understanding Apace Kafka and Apache Flume

Apache spark Streaming - Processing Multiple Batches

Apache Spark Streaming - Data Sources

Project 1- Domain: Finance

Project 2- Domain: Media and Entertainment

Spark Graph

FAQ

What is the salary expectancy of a Machine Learning engineer?

What are the benefits of this course?

Does a Machine Learning engineer require coding?

Do I need to learn the modules in order?

Is it tough to learn Machine Learning?

Why Machine Learning is essential?

What are the top skills required to be a Machine Learning Engineer?