Sale!
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4 Months Data Science and Machine Learning Course

 45,000.00

✓ 2.5 month Long Course

✓ 2 Hours per day

✓ Daily Assignment / Projects / Doubt solving

✓ 24/7 Private Community Support / Revision session

✓ Both Physical / Online available

✓ Verified Certificate

✓ Everyday recording available

✓ Internship guaranteed

Now to bridge the gap between industry and IT students Vrit Technologies is launching, Data Science and Machine Learning with Python Course.

Complete Data Science and Machine Learning Diploma Course with Python covers complete Introduction to Programming Languages, Python, Basics of Data Science, Machine Learning, Deep Learning, Natural Language Processing, Web Interfaces, and More.

Data Science is an interdisciplinary field that involves extracting insights and knowledge from data using various techniques and tools. It encompasses various stages of data processing, such as data collection, data cleaning, data analysis, and data visualization. The aim of data science is to provide valuable insights and knowledge to solve complex problems using data-driven approaches.

Machine learning is a subset of data science that involves the use of statistical algorithms and models to enable computers to learn from data without being explicitly programmed. It focuses on developing algorithms that can learn patterns and make predictions or decisions based on data.

 

Benefits of Data Science and Machine Learning

Data Science and Machine Learning are not just buzzwords; they are transformative technologies that have revolutionized industries across the globe. By enrolling in our course, you can:

  1. Find Insights: Discover patterns and insights hidden within vast datasets, enabling data-driven decision-making.
  2. Predictive Power: Leverage machine learning algorithms to make accurate predictions, from customer preferences to stock market trends.
  3. In-Demand Skills: Gain expertise in technologies that are in demand by employers, ensuring a promising career.
  4. High Earning Potential: Data scientists and machine learning professionals enjoy some of the highest salaries in the IT industry.

 

Objectives of Our Data Science and Machine Learning Course

Our course is designed with clear objectives in mind:

  1. Comprehensive Knowledge: We provide a well-rounded curriculum that covers the fundamentals of data science and machine learning, ensuring a strong foundation.
  2. Hands-On Experience: You’ll work on real-world projects and gain practical skills that are directly applicable in the industry.
  3. Expert Guidance: Learn from industry experts who bring their practical experience into the classroom.
  4. Career Readiness: Our program equips you with the skills and tools necessary to launch a successful career in data science and machine learning.

 

Why Choose Vrit Technologies?

At Vrit Technologies, we stand out as a premier destination for IT training, and here’s why you should choose us:

  1. Proven Track Record: We have a history of successfully training individuals who have gone on to secure rewarding positions in the industry.
  2. Industry-Relevant Curriculum: Our courses are continuously updated to reflect the latest industry trends and technological advancements.
  3. Flexible Learning: We offer both in-person and online training options to accommodate your schedule and learning preferences.
  4. Supportive Community: Join a thriving community of learners and professionals who share your passion for data science and machine learning.
  5. Networking Opportunities: Building a professional network is crucial in the IT industry. When you choose Vrit Technologies, you’ll join a vibrant community of like-minded individuals, including fellow students, alumni, and industry professionals. These connections can be invaluable as you progress in your career.
  6. Industry-Recognized Certification: When you complete your data science and machine learning course at Vrit Technologies, you’ll not only gain knowledge and practical skills but also receive an industry-recognized certification. This certification serves as a testament to your expertise and commitment to excellence in these fields.

 

Career Opportunities as a Data Scientist

The demand for data scientists is on the rise, and the opportunities are endless. As a data scientist, you can find yourself in roles such as:

  • Data Analyst
  • Machine Learning Engineer
  • Business Intelligence Analyst
  • Data Engineer
  • AI Developer
  • and more!

In today’s data-driven world, the skills you gain in this course can open doors to a wide range of industries, from healthcare and finance to e-commerce and technology.

Ready to embark on your journey into data science and machine learning? Join Vrit Technologies, and let us guide you towards a rewarding and promising career.

Enroll now and take the first step towards mastering data science and machine learning at Vrit Technologies!

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Programming Fundamentals {Python Basics}

• Core Data Structures of Python
• Number
• String
• List
• Tuples
• Dictionary
• Set
• Advance Operation on Core Data-Structures
• Decision and Branching
• If, Else if, Else, Break, Continue
• Looping
• Functions
• Lambda Functions
• Map, Reduce, Filter [**]
• Function Recursion
• Decorators [**]

Python Core
  • List and Dictionary Comprehension
  • Exceptions and Exception Handling
  • File Handling
  • Object Oriented Programming (OOP)
  • Introduction to Classes
  • Inheritance, Encapsulation, Polymorphism, Abstraction
  • Method Overloading
  • Building Custom Packages and Modules
Basics to Data Science

• Introduction to Data Science
• Introduction to NumPy and Matplotlib
• Matrix Operations with NumPy
• Random Variable and Probability Distributions
• Probability
• Properties of Probability Distributions
• Mean, Median, Mode
• Variance, Skewness, Kurtosis
• Multivariate Normal Distribution
• Co-Variance, Correlation
• Introduction to Scikit-Learn
• Data Pre-Processing Techniques using Scikit-Learn
• Dimensionality Reduction as Data Pre-Processing
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)

Machine learning – I
  • Introduction to Reinforcement Learning
  • Q-Learning with Python
  • Introduction to Clustering
  • K-Means Clustering
  • Agglomerative Clustering
  • Introduction to Supervised Learning
  • Naive Bayes Classification
Machine learning – II

• Linear and Polynomial Regression
• K-Nearest Neighbors
• Decision Tree
• Balancing Bias vs Variance of ML Model
• Ensemble Learning
• Random Forest and Adaptive Boost
• Identifying Important Features of Data
• Time Series Analysis

Deep learning – I
  • Introduction to Logistic Regression
  • Computation Graph and Gradient Descent
  • Introduction to Artificial Neuron (Perceptron)
  • Multi-Layer Perceptron
  • Introduction to Artificial Neural Networks
  • Designing Artificial Neural Networks with Keras
  • Gradient Decent Variants
  • Classification and Regression using Neural Networks
Deep learning – II

• Introduction to Convolutional Neural Network (CNN)
• Object Classification with CNN
• Standard CNN Architectures
• Introduction to Object Detection
• The YOLO Algorithm
• Transfer Learning
• Deep Reinforcement Learning

Natural language processing + web interface
  • Introduction to NLTK
  • Text Pre-Processing
  • POS Tagging and Named-Entity Recognition
  • Latent Semantic Analysis
  • Introduction to Recurrent Neural Network
  • Word2Vec Algorithm for Text Vectorization
  • Natural Language Processing with LST
  • Giving Web Interface to ML Application using Flask/Django / Streamlit.
Advanced Python and OOP
  • Introduction to Map, Reduce, Filter
  • List and Dictionary Comprehensions
  • Exceptions and Exception Handling
  • File Handling in Python
  • OOP in Python
Machine Learning, Deep Learning, and NLP
  • Dimensionality Reduction as Data Pre-Processing
  • Principal Component Analysis (PCA)
  • Linear and Polynomial Regression
  • K-Nearest Neighbors
  • Decision Tree
  • Balancing Bias vs Variance of ML Model
  • Ensemble Learning: Random Forest and Adaptive Boost
  • Identifying Important Features of Data
  • Introduction to Deep Learning: Logistic Regression, Perceptron, MLP
  • Convolutional Neural Network (CNN)
Assignment / Labs

• Each student will have a project to complete in order to demonstrate their understanding both during and after the course.
• Lab assignments will focus on the practice and mastery of contents covered in the lectures; and introduce critical and fundamental problem-solving techniques to the students.