PG Diploma in Data Science

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Level Up Your Skills with Our Data Science Online Course!

Welcome to AI4Infinity. The best place to get comprehensive data science and machine learning online training. Our cutting-edge courses are carefully designed to give you the skills and knowledge you need to excel in a rapidly evolving field. Whether you want to embark on a new career path or advance your current career path, our online data science and machine learning courses offer a great opportunity to expand your expertise.

Discover the secrets of data analysis and interpretation by enrolling in the online data science course. Our expert-led courses cover a wide range of topics, from basic principles to advanced techniques, ensuring you are well-prepared to tackle real-world challenges. Dive deep into the world of data science with interactive online courses designed for all skill levels.

Whether you are interested in supervised learning, unsupervised learning, or reinforcement learning, our comprehensive curriculum has you covered. Gain hands-on experience building machine learning models and solving complex problems by taking online machine learning courses.

JOB ROLES

By completing our training program, you will be qualified for a variety of positions in this field.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Data Storyteller
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Business Intelligence Developer
  • Database Administrator
  • Statistician

SALARY RANGE

The average salary for a fresher in data science is about USD 95,000 per annum. This is close to Rs 70 lakh per annum. The average salary abroad is between USD 130,000 to USD 195,000 for a mid-level Data Scientist.

What you'll get

Master Machine Learning Fundamentals

Gain a strong foundation in machine learning, starting with the basics and progressing to advanced topics.

Statistical Data Science Expertise:

Develop proficiency in statistical data analysis and scientific methods to make informed decisions.

Python Programming Mastery:

Learn Python for data science, covering variables, lists, loops, libraries, and data manipulation techniques.

Machine Learning Algorithms:

Explore a variety of machine learning algorithms, including regression, decision trees, Naive Bayes, and support vector machines.

Deep Learning and NLP:

Dive into the world of deep learning with neural networks and convolutional neural networks, and understand natural language processing (NLP) techniques.

Hands-On Projects:

Apply your knowledge by working on real-world projects, including a complete end-to-end machine learning project, to gain practical experience.

Course Content

  • 1. Variables, Integers, Booleans
  • 2. Strings and operations on strings
  • 3. Lists and operations on Lists
  • 4. Tuples, Sets, Dictionaries
  • 5. Indentation in Python, Different types of statements in Python, if, elif and else conditions.
  • 6. Loops in Python For and While loop
  • 7. List Comprehension in Python
  • 8. Functions and Methods in Python
  • 9. Objects, Class, Polymorphism, Errors and Exception handling in Python
  • 10. Libraries in Python
  • 11. NumPy
  • 12. Pandas
  • 13. Matplotlib
  • 14. Seaborn
  • 15. Exploratory Data Analysis (EDA) Projects using Python

  • 1. Introduction to basic Statistics terms
  • 2. Types of Statistics
  • 3. Levels of measurement
  • 4. Measures of central tendency
  • 5. Random Variables
  • 6. Covariance and Correlation

  • 1. Probability Density/ Distribution function
  • 2. Types of the Probability Distribution
  • 3. Binomial Distribution
  • 4. Poisson Distribution
  • 5. Normal Distribution
  • 6. Probability Density Function and Mass Function
  • 7. Bernoulli Distribution
  • 8. Uniform Distribution
  • 9. Central Limit Theorem
  • 10. Hypothesis
  • 11. Hypothesis testing
  • 12. P-Value & T-Test
  • 13. Student Distribution
  • 14. Type1 & Type 2 Error
  • 15. Bayes Theorem
  • 16. Confidence Interval
  • 17. Chi-Square test
  • 18. Analysis of Variance(Anova)
  • 19. F- Distribution

  • 1. Introduction to machine Learning
  • 2. Supervised & Unsupervised
  • 3. Train, Test, validation
  • 4. Performance
  • 5. Over fitting & Under fitting
  • 6. Bias vs Variance

  • 1. Regression
  • 2. Linear Regression
  • 3. Gradient descent
  • 4. Multiple Linear regression
  • 5. R square and Adjusted R square
  • 6. Ridge Regression
  • 7. Lasso Regression

  • 1. In-depth mathematical intuition
  • 2. In-depth Geometrical intuition
  • 3. Confusion matrix
  • 4. Precision, Recall and F1 Score

  • 1. Decision Tree - Exclusive

  • 1. Naive Bayes - Exclusive

  • 1. KNN - Exclusive

  • 1. Support Vector Machine (SVM) - Exclusive

  • 1. Bagging Technique
  • 2. Bootstrap Aggregation
  • 3. Random Forest
  • 4. Random Forest Classifier
  • 5. Random Forest Regression

  • 1. Ada Boost
  • 2. Gradient Boost
  • 3. XG Boost

  • 1. Clustering and their types
  • 2. K-means clustering
  • 3. Batch K-means clustering
  • 4. Hierarchical clustering

  • 1. Complete end-to-end project on Machine Learning Algorithms

  • 1. Introduction to Neural Network
  • 2. DetailMathematical intuition
  • 3. Neural Network use cases
  • 4. Activation Functions
  • 5. Loss Functions
  • 6. Optimizers
  • 7. Forward and Backward Propagation
  • 8. Weight initialization Technique
  • 9. Vanishing Gradient Problem
  • 10. Artificial Neural Network (ANN)
  • 11. Convolutional Neural Network (CNN)
  • 12. Implementation of ANN & CNN

  • 1. NLP Basics
  • 2. Tokenization
  • 3. Stop Words
  • 4. Stemming and Lemmatization
  • 5. Word Vectorization
  • 6. TF-IDF
  • 7. Word Embedding
  • 8. Attention Based Model
  • 9. Transfer Learning

  • 1. Everything you need to know about mySQL database

Learner Benifits

Tailored Learning Experience

Our institute offers individualized lessons specifically tailored to each learner’s needs. We enable every student to realize his or her maximum potential as well as achieve their career objectives through customized curricula, flexible educational alternatives and personalized assistance.

Industry-Embedded Training

As part of our curriculum, we work with field professionals so that it remains current, practical and reflective of the trends in the job market. Students get hands-on experience applying the latest tools and techniques, which prepares them to succeed in their chosen fields.

Extensive Placement Support

After graduation, our students can rely on comprehensive placement support services that allow them to enter meaningful employment. From resume writing workshops to interview coaching sessions, our career services staff is dedicated guiding students towards finding a job.

Expert Mentorship

Students have opportunities to interact with experienced professionals who provide invaluable advice about what they are learning in college. These mentor relationships enhance learning while acting as conduits for networking and advancement in careers.

Large Alumni Network

We are proud of owning one of the biggest alumni networks in our region comprising successful professionals in other industries also. As a student you will get access to this network providing you with valuable connections and potential career pathways after training

FAQ

Data science is a multidisciplinary field that involves using techniques from statistics, machine learning, computer science, and domain expertise to extract insights and knowledge from data.
AI is a broad field of computer science that focuses on creating systems and machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions.
A strong foundation in mathematics, particularly statistics and linear algebra, along with programming skills (usually in languages like Python or R), is essential. Knowledge of computer science fundamentals is also valuable.
Machine learning is a subset of AI that encompasses various techniques for making predictions and decisions from data. Deep learning is a subfield of machine learning that focuses on using neural networks, particularly deep neural networks, for tasks like image and speech recognition.
The demand for data scientists and AI professionals is high across various industries, including finance, healthcare, tech, and more. Career prospects include roles such as data analyst, machine learning engineer, data engineer, AI researcher, and data scientist.
Common tools include Python/R, libraries like TensorFlow and scikit-learn, Jupyter notebooks, and data visualization tools like Matplotlib and Tableau. Cloud platforms like AWS, Azure, and Google Cloud are also important.
You can work on personal projects, participate in online competitions (e.g., Kaggle), internships, and contribute to open-source projects. Online courses and bootcamps also offer hands-on experience.
Showcase your projects, including problem statements, data preprocessing, modeling techniques, and results. Explain your approach and the impact of your work. A well-documented portfolio is essential for job applications.
While advanced degrees can be beneficial, many roles in data science and AI can be entered with a bachelor's degree. Continuous learning, certifications, and practical experience are also highly valued.

Ai4Infinity

AI4Infinity is a leading institution dedicated to providing world-class education and training in data science, analytics, machine learning and artificial intelligence (AI).

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