Online & Classroom

Machine learning with pyspark Training Course

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Machine learning with pyspark ?

Why Data Science
In-Memory Computation in Spark

With in-memory processing, it helps you increase the speed of processing. And the best part is that the data is being cached, allowing you not to fetch data from the disk every time thus the time is saved.

Why Data Science
Swift Processing

When you use PySpark, you will likely to get high data processing speed of about 10x faster on the disk and 100x faster in memory. By reducing the number of read-write to disk, this would be possible.

Why Data Science
Fault Tolerance in Spark

Through Spark abstraction-RDD, PySpark provides fault tolerance. The programming language is specifically designed to handle the malfunction of any worker node in the cluster, ensuring that the loss of data is reduced to zero

Who is This program for

  • Data Scientists,Data Engineers,Data Analysts
  • BI Professionals,Research professionals,Software Architects
  • Testing Professionals
  • Anyone who is looking to upgrade Big Data skills
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

  • DataGeneration
  • Spark
  • Spark Core
  • Spark Components
  • Setting Up Environment
  • Windows
  • Anaconda Installation
  • Java Installation
  • Spark Installation
  • IOS
  • Docker
  • Databricks
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Load and Read Data
  • Adding a New Column
  • Filtering Data
  • Condition 1
  • Condition 2
  • Distinct Values in Column
  • Grouping Data
  • Aggregations
  • User-Defined Functions (UDFs)
  • Traditional Python Function
  • Using Lambda Function
  • Pandas UDF (Vectorized UDF)
  • Pandas UDF (Multiple Columns)
  • Drop Duplicate Values
  • Delete Column
  • Writing Data
  • CSV
  • Parquet
  • Linear Regression
  • Variables
  • Theory
  • Interpretation
  • Evaluation
  • Code
  • Create the SparkSession Object Step
  • Read the Dataset Step
  • Exploratory Data Analysis Step
  • Feature Engineering Step
  • Splitting the Dataset Step
  • Build and Train Linear Regression Model Step
  • Evaluate Linear Regression Model on Test Data
  • Probability
  • Using Linear Regression
  • Using Logit
  • Interpretation (Coefficients)
  • Dummy Variables
  • Model Evaluation
  • True Positives
  • True Negatives
  • False Positives
  • False Negatives
  • Accuracy
  • Recall
  • Precision
  • F1 Score
  • Cut Off /Threshold Probability
  • ROC Curve
  • Logistic Regression Code
  • Data Info
  • Create the Spark Session Object
  • Read the Dataset
  • Exploratory Data Analysis
  • Feature Engineering
  • Splitting the Dataset
  • Build and Train Logistic Regression Model
  • Training Results
  • Evaluate Linear Regression Model on Test Data
  • Confusion Matrix
  • Decision Tree
  • Entropy
  • Information Gain
  • Random Forests
  • Code
  • Data Info
  • Create the SparkSession Object
  • Read the Dataset
  • Exploratory Data Analysis
  • Feature Engineering
  • Splitting the Dataset
  • Build and Train Random Forest Model
  • Evaluation on Test Data
  • Accuracy
  • Precision
  • AUC
  • Saving the Model
  • Recommendations
  • Popularity Based RS
  • Content Based RS
  • Collaborative Filtering Based RS
  • Hybrid Recommender Systems
  • Code
  • Data Info
  • Create the SparkSession Object
  • Read the Dataset
  • Exploratory Data Analysis
  • Feature Engineering
  • Splitting the Dataset
  • Build and Train Recommender Model
  • Predictions and Evaluation on Test Data
  • Recommend Top Movies That Active User Might Like
  • Starting with Clustering
  • Applications
  • K-Means
  • Hierarchical Clustering
  • Code
  • Data Info
  • Create the SparkSession Object
  • Read the Dataset
  • Exploratory Data Analysis
  • Feature Engineering
  • Build K-Means Clustering Model
  • Visualization of Clusters
  • Introduction
  • Steps Involved in NLP
  • Corpus
  • Tokenize
  • Stopwords Removal
  • Bag of Words
  • Count Vectorizer
  • TF-IDF
  • Text Classification Using Machine Learning
  • Sequence Embeddings
  • Embeddings
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Event Data Analysis using AWS ELK Stack

This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation

Insurance Pricing Forecast Using Regression Analysis

In this project, we are going to talk about insurance forecast by using regression techniques

Sentiment Analysis of Twitter Data using PySpark and Live Graphs

In this project, Sentiment Analysis Application is developed using Pyspark which is combination of Apache Spark and Python. This application fetches Twitter data in live stream and classifies tweets into positive and negative categories


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Learn From Home

First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

Learn From Home

Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

Learn From Home

Learn from Classroom

Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

Get in touch with us

What do you benefit from this programs
  • What do you benefit from this programs
  • Master the concepts on PySpark
  • In-depth exercises and real-time projects on PySpark
  • Learn about Apache Spark Core, Spark Internals, RDD, Spark SQL, etc

I’m Interested

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