PG Diploma in Data Science and Machine Learning
100% JOB Assured with Globally Accepted Certificate
Intermediate
PG Diploma in Data Science and Machine Learning
100% JOB Assured with Globally Accepted Certificate
Overview
PG Diploma in Data Science and Machine Learning
Description
PG Diploma in Data Science
PG Diploma in data science and machine learning with placement guarantee is a comprehensive programs at Cranes Varsity which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.
An experienced person without Data Science knowledge can make a career as a Data Scientist. While having a strong foundation in Data Science concepts and techniques is advantageous, it is not always a prerequisite for starting a career as a Data Scientist.
After completion of the Data Science course, you will develop expertise in several key areas. Firstly, you will learn data manipulation and analysis techniques using programming languages like Python. You will gain proficiency in statistical analysis and data visualization to extract insights from complex datasets. Machine learning skills will enable you to build predictive models, classification algorithms, and clustering techniques. Deep learning will empower you to work with neural networks for advanced tasks like image and text analysis. Additionally, you will acquire knowledge in data preprocessing, model evaluation, and deployment. These skills will equip you to tackle real-world challenges and unlock exciting opportunities in data-driven industries.
These capabilities will enable you to pursue various roles in the field of data science and machine learning, such as data scientist, machine learning engineer, data analyst, or AI specialist, across industries ranging from finance and healthcare to e-commerce and marketing.
Our dedicated career development sessions, resume building assistance, and interview preparation help you to enhance your employability. Our strong industry connections and collaborations enable us to provide job placement assistance, connecting you with top companies in the automotive sector. We take pride in our high placement record and strive to help you kick-start a successful career in embedded and automotive systems.
An experienced person without Data Science knowledge can make a career as a Data Scientist. While having a strong foundation in Data Science concepts and techniques is advantageous, it is not always a prerequisite for starting a career as a Data Scientist.
PG Diploma in data science and machine learning is comprehensive programs at Cranes Varsity which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.
After completion of the course, you will develop expertise in several key areas. Firstly, you will learn data manipulation and analysis techniques using programming languages like Python. You will gain proficiency in statistical analysis and data visualization to extract insights from complex datasets. Machine learning skills will enable you to build predictive models, classification algorithms, and clustering techniques. Deep learning will empower you to work with neural networks for advanced tasks like image and text analysis. Additionally, you will acquire knowledge in data preprocessing, model evaluation, and deployment. These skills will equip you to tackle real-world challenges and unlock exciting opportunities in data-driven industries.
These capabilities will enable you to pursue various roles in the field of data science and machine learning, such as data scientist, machine learning engineer, data analyst, or AI specialist, across industries ranging from finance and healthcare to e-commerce and marketing.
Our dedicated career development sessions, resume building assistance, and interview preparation help you to enhance your employability. Our strong industry connections and collaborations enable us to provide job placement assistance, connecting you with top companies in the automotive sector. We take pride in our high placement record and strive to help you kick-start a successful career in embedded and automotive systems.
The PG Diploma in Data Science and Machine Learning is a five-month professional program that provides in-depth data science knowledge and expertise.
Cranes mentors engineers in all critical disciplines to assist them in excelling at designing Data Science based applications that meet industry standards.
Cranes provide students with a structured framework to help them develop technical skills and knowledge. The lectures are well-planned and delivered with examples to make them more interesting and understandable. We want to help students develop a much broader range of mental representations of knowledge.
Cranes Varsity is considered as the best Data Science Course (Available Online) which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including Genpact, Ernst & Young, Capgemini, Vodafone, CGI, Wipro, Tata Elxsi, IBM, Lumen Technologies, Tech Mahindra, Birla Soft, HTC, Happiest Minds, Western Digital, Mearsk Global, Koireader, K7 Computing, Mphasis, Atos, Latent View, etc.
PG Diploma in Data Science with Placement
We offer the highest quality teaching, assessment and placement support through our Data Science course. The course is designed to make a novice into an expert from developing Python programming, and writing queries on SQL to building Machine learning & Deep Learning Models and Cloud computing. Our Lead mentors are industry experts and have been associated with us for decades.
If you’re looking toward building your career in Data Science and are interested in getting the Data Science Course with Placement, then Cranes Varsity is the right destination for realizing your aspirations and growing on your Career ladder.
Data Science using Python training course syllabus is classified into modules that help students better understand the subject. Which are listed below:
PG Diploma in Data Science Course Modules
Generic
- RDBMS using MySQL
- Python for Data Science
- Advanced Python (Testing and Web Scraping)
- Exploratory Data Analysis using Pandas
Data Science Specialization
- Mathematics and Statistics for Data Science
- Machine Learning using sklearn
- Machine Learning model Improvement
- Deep Learning using Tensor Flow
- Data Analysis and Visualization using Tableau
- Natural Language Processing
Projects
- Apply statistical methods to make decisions in various business problems, including bank, stock markets, etc.
- Apply regression to predict future flight price
- Apply classification to classify customer
- Use clustering to cluster banking customers
- Computer vision projects like Face recognition, Image Quality Improvement, etc.
Platform
- Anaconda Distribution Jupyter, Spyder
- Tableau
- Google Colab
Course Content
- Introduction to databases and RDBMS,
- Database creation, concept of relation and working examples
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Read, update and delete operations on tables. Working with nulls
- Querying tables: Select statement, examples and its variations
- Filtering, Sorting, Predicates and working examples
- Joins in SQL and working examples
- Insert, Update, Delete operations and working examples
- Scalar functions in SQL and working examples
- SQL set based operations and data aggregation
- Sub-queries in SQL
- Normalization and de-normalization: Views and Temporary tables
- Transactions in SQL
- SQL programming Creating stored procedures, Cursors in SQL
- EBS(Elastic Block Storage),VPC
- EBS volumes and Snapshots
- RDS
- Introduction to Python
- Python Data types and Conditions
- Control Statements
- Python Functions
- Default arguments
- Functions with variable number of args
- Scope of Variables
- Global specifier
- Working with multiple files
- List and Tuple
- List Methods
- List Comprehension
- Map and filter functions
- String
- List comprehension with conditionals
- Set and Dictionary
- Exception Handling
- File Handling
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- Pandas Data frame
- Selection Data (loc, iloc)
- Filtering Data Frames
- Working with Categorical Data
- Grouping & Aggregation
- Merging Data Frame(concat, merge)
- Sorting Data Frames
- Importing csv files
- Importing Excel Files
- Creating graphs using Matplotlib
- Customizing Plots
- Saving Plots
- Scatter Plot, Line Graph
- Bar Graph, Histogram
- Subplots
- Seaborn
- Matplotlib
- Importing Data in PowerBI
- Data Preparation in Power BI
- Data Modelling in Power BI
- Filtering Visualizing Data
- Reports in Power BI
- Introduction to DAX in Power BI
- Logarithm
- Python Scipy Library
- Data Preprocessing
- Standard Deviation
- Probability and Distribution
- Handling missing data
- Descriptive and Inferential Statistics
- Binomial Theorem
- Onehot Encoding
- Mean, Median, Mode
- Hypothesis testing
- Label encoding
- Percentile,
- Inferential Statistics
- Standardization and normalization
- Log Normal Distribution
- Chi-square test, T test
- Binning
- Mean Absolute Deviation,
- Ordinal, frequency encoding
- Transformation
- Handson Examples
- Case study: To perform Data cleaning and statistical analysis
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Intro to Analyzing Data Using Spreadsheets
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Charting techniques in Excel
- Interactive dashboard creation
- Data analytics project using Excel
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Regular Expression
- Finding Patterns of Text
- Meta characters
- Testing Fundamentals
- Unit Testing with Pytest
- Working with JSON
- Decorators
- UI Development with Tkinter
- Containers
- Iterators
- UI development Mini Project
- What is Deep Learning
- Hidden Layers
- Building project based on CNN
- Deep Learning Methods
- Activation Function
- Tensorflow, pytorch, Keras
- Deep Learning Application
- Forward and Backward propagation
- Batch Normalization, dropout
- Artificial Neural Network
- Deep Learning Libraries
- Performance measure for ANN
- CNN architecture
- CNN for computer vision
- Need for Hardwares in Deep learning
- Computer vision basics
- OpenCV
- Working with Images
- Edge detection
- Filtering
- Object detection
- Transfer Learning
- Pretrained models,
- Restnet50, Imagenet, Mobilenet
- Optimizing regression models with forward elimination, grid search cv
- Improving classification models with Ensemble modeling
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Regularization L1 and L2 regularization
- Random Forest, Bagging
- Dimensionality Reduction with PCA
- Clustering and types
- Evaluate clustering results, Elbow Plot
- Train test split the data
- Kmeans Clustering
- Hierarchical clusterin
- Hyperparameter Tuning
- ML Project
- ML Project
- Stacking and Voting
- Introduction to databases and RDBMS,
- Database creation, concept of relation and working examples
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Read, update and delete operations on tables. Working with nulls
- Querying tables: Select statement, examples and its variations
- Filtering, Sorting, Predicates and working examples
- Joins in SQL and working examples
- Insert, Update, Delete operations and working examples
- Scalar functions in SQL and working examples
- SQL set based operations and data aggregation
- Sub-queries in SQL
- Normalization and de-normalization: Views and Temporary tables
- Transactions in SQL
- SQL programming Creating stored procedures, Cursors in SQL
- EBS(Elastic Block Storage),VPC
- EBS volumes and Snapshots
- RDS
- Introduction to Python
- Python Data types and Conditions
- Control Statements
- Python Functions
- Default arguments
- Functions with variable number of args
- Scope of Variables
- Global specifier
- Working with multiple files
- List and Tuple
- List Methods
- List Comprehension
- Map and filter functions
- String
- List comprehension with conditionals
- Set and Dictionary
- Exception Handling
- File Handling
- Introduction to Python
- Python Data types and Conditions
- Control Statements
- Python Functions
- Default arguments
- Functions with variable number of args
- Scope of Variables
- Global specifier
- Working with multiple files
- List and Tuple
- List Methods
- List Comprehension
- Map and filter functions
- String
- List comprehension with conditionals
- Set and Dictionary
- Exception Handling
- File Handling
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- Pandas Data frame
- Selection Data (loc, iloc)
- Filtering Data Frames
- Working with Categorical Data
- Grouping & Aggregation
- Merging Data Frame(concat, merge)
- Sorting Data Frames
- Importing csv files
- Importing Excel Files
- Creating graphs using Matplotlib
- Customizing Plots
- Saving Plots
- Scatter Plot, Line Graph
- Bar Graph, Histogram
- Subplots
- Seaborn
- Matplotlib
- Logarithm
- Python Scipy Library
- Mean Absolute Deviation,
- Standard Deviation
- Probability and Distribution
- Normal Distribution and Z Score
- Descriptive and Inferential Statistics
- Binomial Theorem
- Visualizing Data
- Mean, Median, Mode
- Hypothesis testing
- Variance: ANOVA
- Percentile,
- Inferential Statistics
- Statistical Significance
- Log Normal Distribution
- Chi-square test, T test
- Data Preprocessing
- Standardization and normalization
- Ordinal, frequency encoding
- Transformation
- Tableau Introduction
- Traditional Visualization vs Tableau
- Tableau Architecture
- Working with sets
- Creating Groups
- Data types in Tableau
- Connect Tableau with Different Data Sources
- Visual Analytics
- Parameter Filters
- Cards in Tableau
- Charts, Dash-board
- Joins and Data Blending
- Tableau Calculations using Functions
- Building Predictive Models
- Dynamic Dashboards and Stories
- Logarithm
- Python Scipy Library
- Data Preprocessing
- Standard Deviation
- Probability and Distribution
- Handling missing data
- Descriptive and Inferential Statistics
- Binomial Theorem
- Onehot Encoding
- Mean, Median, Mode
- Hypothesis testing
- Label encoding
- Percentile,
- Inferential Statistics
- Standardization and normalization
- Log Normal Distribution
- Chi-square test, T test
- Binning
- Mean Absolute Deviation,
- Ordinal, frequency encoding
- Transformation
- Handson Examples
- Case study: To perform Data cleaning and statistical analysis
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Intro to Analyzing Data Using Spreadsheets
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Charting techniques in Excel
- Interactive dashboard creation
- Data analytics project using Excel
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Regular Expression
- Finding Patterns of Text
- Meta characters
- Testing Fundamentals
- Unit Testing with Pytest
- Working with JSON
- Decorators
- UI Development with Tkinter
- Containers
- Iterators
- UI development Mini Project
- Optimizing regression models with forward elimination, grid search cv
- Improving classification models with Ensemble modeling
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Regularization L1 and L2 regularization
- Random Forest, Bagging
- Dimensionality Reduction with PCA
- Clustering and types
- Evaluate clustering results, Elbow Plot
- Train test split the data
- Kmeans Clustering
- Hierarchical clusterin
- Hyperparameter Tuning
- ML Project
- ML Project
- Stacking and Voting
- What is Deep Learning
- Hidden Layers
- Building project based on CNN
- Deep Learning Methods
- Activation Function
- Tensorflow, pytorch, Keras
- Deep Learning Application
- Forward and Backward propagation
- Batch Normalization, dropout
- Artificial Neural Network
- Deep Learning Libraries
- Performance measure for ANN
- CNN architecture
- CNN for computer vision
- Need for Hardwares in Deep learning
- Computer vision basics
- OpenCV
- Working with Images
- Edge detection
- Filtering
- Object detection
- Transfer Learning
- Pretrained models,
- Restnet50, Imagenet, Mobilenet
- Introduction to databases and RDBMS,
- Database creation, concept of relation and working examples
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Read, update and delete operations on tables. Working with nulls
- Querying tables: Select statement, examples and its variations
- Filtering, Sorting, Predicates and working examples
- Joins in SQL and working examples
- Insert, Update, Delete operations and working examples
- Scalar functions in SQL and working examples
- SQL set based operations and data aggregation
- Sub-queries in SQL
- Normalization and de-normalization: Views and Temporary tables
- Transactions in SQL
- SQL programming Creating stored procedures, Cursors in SQL
- EBS(Elastic Block Storage),VPC
- EBS volumes and Snapshots
- RDS
- Introduction to Python
- Python Data types and Conditions
- Control Statements
- Python Functions
- Default arguments
- Functions with variable number of args
- Scope of Variables
- Global specifier
- Working with multiple files
- List and Tuple
- List Methods
- List Comprehension
- Map and filter functions
- String
- List comprehension with conditionals
- Set and Dictionary
- Exception Handling
- File Handling
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- Pandas Data frame
- Selection Data (loc, iloc)
- Filtering Data Frames
- Working with Categorical Data
- Grouping & Aggregation
- Merging Data Frame(concat, merge)
- Sorting Data Frames
- Importing csv files
- Importing Excel Files
- Creating graphs using Matplotlib
- Customizing Plots
- Saving Plots
- Scatter Plot, Line Graph
- Bar Graph, Histogram
- Subplots
- Seaborn
- Matplotlib
- Logarithm
- Python Scipy Library
- Mean Absolute Deviation,
- Standard Deviation
- Probability and Distribution
- Normal Distribution and Z Score
- Descriptive and Inferential Statistics
- Binomial Theorem
- Visualizing Data
- Mean, Median, Mode
- Hypothesis testing
- Variance: ANOVA
- Percentile,
- Inferential Statistics
- Statistical Significance
- Log Normal Distribution
- Chi-square test, T test
- Data Preprocessing
- Standardization and normalization
- Ordinal, frequency encoding
- Transformation
- Tableau Introduction
- Traditional Visualization vs Tableau
- Tableau Architecture
- Working with sets
- Creating Groups
- Data types in Tableau
- Connect Tableau with Different Data Sources
- Visual Analytics
- Parameter Filters
- Cards in Tableau
- Charts, Dash-board
- Joins and Data Blending
- Tableau Calculations using Functions
- Building Predictive Models
- Dynamic Dashboards and Stories
- Logarithm
- Python Scipy Library
- Data Preprocessing
- Standard Deviation
- Probability and Distribution
- Handling missing data
- Descriptive and Inferential Statistics
- Binomial Theorem
- Onehot Encoding
- Mean, Median, Mode
- Hypothesis testing
- Label encoding
- Percentile,
- Inferential Statistics
- Standardization and normalization
- Log Normal Distribution
- Chi-square test, T test
- Binning
- Mean Absolute Deviation,
- Ordinal, frequency encoding
- Transformation
- Handson Examples
- Case study: To perform Data cleaning and statistical analysis
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Intro to Analyzing Data Using Spreadsheets
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Charting techniques in Excel
- Interactive dashboard creation
- Data analytics project using Excel
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Regular Expression
- Finding Patterns of Text
- Meta characters
- Testing Fundamentals
- Unit Testing with Pytest
- Working with JSON
- Decorators
- UI Development with Tkinter
- Containers
- Iterators
- UI development Mini Project
- Optimizing regression models with forward elimination, grid search cv
- Improving classification models with Ensemble modeling
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Regularization L1 and L2 regularization
- Random Forest, Bagging
- Dimensionality Reduction with PCA
- Clustering and types
- Evaluate clustering results, Elbow Plot
- Train test split the data
- Kmeans Clustering
- Hierarchical clusterin
- Hyperparameter Tuning
- ML Project
- ML Project
- Stacking and Voting
- What is Deep Learning
- Hidden Layers
- Building project based on CNN
- Deep Learning Methods
- Activation Function
- Tensorflow, pytorch, Keras
- Deep Learning Application
- Forward and Backward propagation
- Batch Normalization, dropout
- Artificial Neural Network
- Deep Learning Libraries
- Performance measure for ANN
- CNN architecture
- CNN for computer vision
- Need for Hardwares in Deep learning
- Computer vision basics
- OpenCV
- Working with Images
- Edge detection
- Filtering
- Object detection
- Transfer Learning
- Pretrained models,
- Restnet50, Imagenet, Mobilenet
Generic:
Relational Database – SQL – 10 Days
- Introduction to databases and RDBMS
- Read, update and delete operations on tables. Working with nulls
- Joins in SQL and working examples
- SQL set based operations and data aggregation, Sub-queries in SQL
- EBS (Elastic Block Storage),VPC
- Database creation, concept of relation and working examples
- Querying tables: Select statement, examples and its variations
- Insert, Update, Delete operations and working examples
- Normalization and de-normalization: Views and Temporary tables, Transactions in SQL
- EBS volumes and Snapshots
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Filtering, Sorting, Predicates and working examples
- Scalar functions in SQL and working examples
- SQL programming, Creating stored procedures, Cursors in SQL
- RDS
Python Programming – 10 Days
- Introduction to Python
- Python Functions
- Scope of Variables
- List and Tuple
- Map and filter functions
- Set and Dictionary
- Python Data types and Conditions
- Default arguments
- Global specifier
- List Methods
- String
- Exception Handling
- Control Statements
- Functions with variable number of args
- Working with multiple files
- List Comprehension
- List comprehension with conditionals
- File Handling
Exploratory Data Analysis with Pandas – 10 Days
- NumPy
- Slicing of Matrices
- NumPy Functions across axis
- Pandas Series
- Pandas Data Frame
- Working with Categorical Data
- Sorting Data Frames
- Creating graphs using Matplotlib
- Scatter Plot, Line Graph
- Seaborn
- Vectorization
- Filtering
- Stacking of arrays
- Data Cleaning
- Selection Data (loc, iloc)
- Grouping & Aggregation
- Importing csv files
- Customizing Plots
- Bar Graph, Histogram
- Matplotlib
- Broadcasting
- Array Creation Functions
- Matrix Calculation
- Handling Missing Data
- Filtering Data Frames
- Merging Data Frame (concat, merge)
- Importing Excel Files
- Saving Plots
- Subplots
Foundational Statistics 5 Days
- Logarithm
- Standard Deviation
- Descriptive and Inferential Statistics
- Mean, Median, Mode
- Percentile
- Log Normal Distribution
- Standardization and normalization
- Python Scipy Library
- Probability and Distribution
- Binomial Theorem
- Hypothesis testing
- Inferential Statistics
- Chi-square test, T test
- Ordinal, frequency encoding
- Mean Absolute Deviation
- Normal Distribution and Z Score
- Visualizing Data
- Variance: ANOVA
- Statistical Significance
- Data Preprocessing
- Transformation
Data Analysis and Visualization Using Tableau –7 Day
- Tableau Introduction
- Working with sets
- Connect Tableau with Different Data Sources
- Cards in Tableau
- Tableau Calculations using Functions
- Traditional Visualization vs Tableau
- Creating Groups
- Visual Analytics
- Charts, Dash-board
- Building Predictive Models
- Tableau Architecture
- Data types in Tableau
- Parameter Filters
- Joins and Data Blending
- Dynamic Dashboards and Stories
Data Science Specialization:
Foundational Machine learning – 7 Days
- Logarithm
- Standard Deviation
- Descriptive and Inferential Statistics
- Mean, Median, Mode
- Percentile
- Log Normal Distribution
- Mean Absolute Deviation
- Handson Examples
- Python Scipy Library
- Probability and Distribution
- Binomial Theorem
- Hypothesis testing
- Inferential Statistics
- Chi-square test, T test
- Ordinal, frequency encoding
- Data Preprocessing
- Handling missing data
- Onehot Encoding
- Label encoding
- Standardization and normalization
- Binning
- Transformation
- Case study: To perform Data cleaning and statistical analysis
Data Analysis and Visualization Using Excel –7 Day
- Introduction to excel
- Intro to Analyzing Data Using Spreadsheets
- Charting techniques in Excel
- Viewing, Entering, and Editing Data
- Converting Data with Value and Text
- Interactive dashboard creation
- Introduction to Data Quality
- Apply logical operations to data using IF
- Data analytics project using Excel
Advanced Python and Unit Testing- 10 Days
- Object Oriented Programming
- Regular Expression
- Testing Fundamentals
- Decorators
- Iterators
- Overloading Operator
- Finding Patterns of Text
- Unit Testing with Pytest
- UI Development with Tkinter
- UI development Mini Project
- Inheritance
- Meta characters
- Working with JSON
- Containers
Advanced Machine Learning and Model Improvement – 10Days
- Optimizing regression models with forward elimination, grid search cv
- Regularization L1 and L2 regularization
- Clustering and types
- Kmeans Clustering
- ML Project
- Improving classification models with Ensemble modeling
- Random Forest, Bagging
- Evaluate clustering results, Elbow Plot
- Hierarchical clustering
- SVM
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Dimensionality Reduction with PCA
- Train test split the data
- Hyperparameter Tuning
- Stacking and Voting
Deep Learning using TensorFlow – 10Days
- What is Deep Learning
- Deep Learning Methods
- Deep Learning Application
- Artificial Neural Network
- CNN architecture
- Computer vision basics
- Edge detection
- Transfer Learning
- Hidden Layers
- Activation Function
- Forward and Backward propagation
- Deep Learning Libraries
- CNN for computer vision
- OpenCV
- Filtering
- Pretrained models
- Building project based on CNN
- Tensorflow, pytorch, Keras
- Batch Normalization, dropout
- Performance measure for ANN
- Need for Hardwares in Deep learning
- Working with Images
- Object detection
Capstone Project on using DL Methods : 5 Days
- Capstone Title Selection
- Project Basic model
- Project report Submission
- Abstract submission
- Interim Report
- Literature Survey
- Final Model Deployment with Pipeline
Placement Statistics
FAQs
How is Cranes Varsity PG diploma in Data Science and Machine Learning program?
The PG diploma in data science by Cranes Varsity is a 5-month program designed specifically for working professionals to develop practical knowledge and skills, and accelerate their data science careers.
What are the career opportunities in Data Science?
Career opportunities are very high with high pay packages compared to other domains, Roles like Data scientist, Data analyst, business analyst, Data analytics manager, Business intelligence manager and many other roles can be expected.
What are the prerequisites to learn Data Science?
Having prior knowledge of any programming language is an added advantage but not mandatory, and the basics of linear algebra are a must.
What are the prerequisites for Machine Learning Course enrollment?
Having a good knowledge of Python Programming concepts, and a good hold on linear algebra.
Can I learn Machine Learning Online?
Yes, you can learn Machine Learning Course completely online.
Testimonials
Ankita Saigal
Placed in Robert Bosch
First of all, I would like to extend my thanks to each and every member of Cranes Varsity. We were taught from the very basics of Embedded Systems Design which made it easier for students from all levels. I would like to extend my vote of thanks to cranes varsity to provide me with numerous opportunities.
Santhosh SM
Placed in L&T Technology
Cranes are one of the top embedded training institutes in Bangalore. It has been a wonderful learning experience in Cranes Varsity. The training in every module of embedded systems at Cranes was effective. It provides a good platform for embedded systems. Cranes helped me get a job in the embedded industry.
Mayur MN
Placed in L&T Technology
It was a great experience in Cranes. My dream was to get into the embedded domain. As a fresher, it is difficult to get into the Embedded Design field, but Cranes made a huge difference in my career by giving the best training and placement assistance provided by Cranes. I would like to say Cranes is the best to choose for those who dream of embedded opportunity.
Chandru V
Placed in Avin Systems
I take this opportunity to thank “CRANES VARSITY”, one of the best-embedded training institutes which are helping students to get into the best company to build their career. I thank all the trainers who enhanced my knowledge in every subject and the placement team for giving me the best opportunities in the field of embedded. Thank you for all your support.
Hemanth Kumar
Placed in Caravel Info Systems
Cranes varsity is the best Embedded Training Institute to learn both practical and theoretical knowledge. It is the best place to gear up your career in a core embedded industry. Management and faculty member support till you get placed. They provided lots of opportunities to me. The embedded Course modules that we learnt here are systematic, and I immensely earned great knowledge.
Ankit Ahalawat
Placed in AK Aerotek Software
I am happy for Cranes for giving a platform and providing opportunities for attending the interview. Modules test, Mock test really helps to clear any company written test/ interview. Trainers were excellent at explaining and clarifying the doubts. I am very thankful to Cranes Varsity.
Nithin G
Placed in Moschip Semiconductor
Cranes varsity is the best platform to improve your technical skills in Embedded System Design. Their dedication towards teaching modules and interaction with the students is commendable, which made me achieve good skills for my career growth in the electronics/semiconductor industry.
Amitha Pankaj
Placed in Lekha Wireless
Happy to say that I am placed in Lekha Wireless. Cranes are one of the best Embedded Training Institutes. The way of teaching in Cranes is good. I thank the management and faculty for the guidance and opportunity.
Sidharth S
Placed in L&T Technology
If not Cranes, I would have been doing a job of not my interest and passion. Cranes provided me with the platform to start my career and knowledge about corporate life and requirements. “Thank you, Cranes” would be an understatement.