Data Science Course in Surat
Data science is a field of study that uses computers math, statistics and artificial intelligence study massive amounts of data to gain actionable business insights. Data scientists can provide important insights into past events as well as their causes future projections, as well as possible actions to take by finding patterns and trends. Businesses can gain an competitive edge, enhance their processes, and take informed decisions based on these insights. Desktop Publishing or DTP Course in Surat is also offered at our institute.
Artificial Intelligence and Machine Learning Course in Surat
A solid basis in Python programming and its applications in the fields of artificial intelligence and machine learning are what the Python for Artificial Intelligence and Machine Learning course aims to give students. The objective of this course is to provide students with the knowledge they need to apply AI/ML algorithms, interact with popular libraries and frameworks, and create useful AI/ML applications using Python. We also offer AI ML Courses in Ahmedabad for Python.
Course Duration 18 Months
Daily Time 2 Hours
Eligibility For This Course
- BCA , MCA , CE , IT , 12 Pass - Science field , Statistics field Background
Included In This Course
- Job Support
- Rich Learning Content
- Taught by Experienced Prof.
- Industry Oriented Projects
Course Modules
Foundation of AI/ML
- Introduction & Fundamentals of Python
- Datatype in details
- control structure & Looping
- Function, Array & Sorting
- object-oriented programming (oop)
- Exception Handling
- File Handing
- modules and Packages
- Regex and cla
- os & subprocess Modules
- web scraping
Advanced AI/ML
- Statistics and use case in data science
- Data in Statistics & Applications of Statistics
- Numerical, Categorical Data
- Population vs. Sample | Definitions, Differences & Examples
- Types of Statistics
- Representation of Data
- Central Limit Theorem
- Probability of an event
- Relationship between variables
- Fundamentals of linear algebra
- Time series Analysis
- Advanced Statistics
- What is a Database?
- What is SQL?
- Intro to Server
- CRUD operation with xampp
- SQL Queries
- Download and install the package
- MySQL connector Python module
- CRUD operations with Python MySQL connectore
- What is Pandas?
- What is NumPy?
- Difference between Pandas and NumPy
- Numpy And Pandas Operations for Data Science
- Importing libraries and datasets
- Data Preprocessing
- Data Wrangling & Exploratory Data Analysis
- Data Cleaning
- Missing Data
- Categorical Data
- Splitting Data into Training and Testing set
- Feature Engineering
- Data Normalization and Encoding techniques
- Creating a data preprocessing Notebook
- Matplotlib & Seaborn for Data Science
- Charts, Pie charts, Scatter and bubble charts
- Bar charts, Column charts, Line charts, Maps
-
Regression Algorithms - Details About Every
Algorithm
- Simaple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Supprot Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Bias - variance trade-off
- L1 and L2 Regularization
- Evaluating Regression Model Performance
- Small Projects
-
Classification
- Details About every Algorithm
- Logistic Regression
- K - Nearest Neighbors
- Support Vectore Machine
- Kernel SVM
- Naive Bays
- Decision Tree Classifier
- Random Forest Classifier
- Evaluating Classification Model Performance
- Small Projects
-
Clustering
- K - Means clustering
- Hierarchal clustering
- DBSCAN
- Recommender System
- Association Rule Learing
- Apriori algorithm
- Small Projects
- ANN - Artificial neural networks
- Computer vision and Image Processing
- CNN - Convolutional Neural Network
- Data Augmentations - Image Analysis and Processing
- Transfer Learning
- Multiclass classification
- Deep Convolution Model - Details About Every Model
- Small Projects
- Natural Language Processing - NLTK and spaCy
- Tokenization, Stemming, Lemmatization, Corpus, Stop Words, Parts-of-speech (POS) Tagging etc..
- Sentiment in Text Data
- Term frequency Inverse document frequency (TF-IDF)
- Vectorization/Word Embedding
- Word Cloud for Text Data
- NLP Model - Details About every Model
- Small Projects
- Markov Decision Process
- Thompos Sampling
- Upper Confidence Bound
- Principle Component Analysis
- Linear Discrimination Analysis
- Kernel PCA
- Random Train/Test Split
- Resampling
- Lasso Regression
- Ridge Regression
- Probabilistic Model Selection
- Boosting and Bagging
- Random Forest
- XGBM
- Module Assignments
- End to End project Description with deployment using Python
Who can join?
- Students: People studying subjects like computer science, math, or statistics might be interested in learning data science.
- Professionals: PPeople who already have jobs, like those in finance, marketing, healthcare, or technology, might want to learn data science to improve their careers.
- IT Professionals: People who work in the IT industry, like software engineers or data analysts, may want to learn data science to gain new skills.
- Researchers: People who do research in fields like social sciences or economics might want to learn data science to analyze and understand their data better.
- Business Analysts: People who work in business analysis or market research can benefit from learning data science to make better decisions using large amounts of data.
- Entrepreneurs: People who start or run businesses can use data science to make their businesses more successful.
- Anyone curious: Data science is interesting, and anyone who wants to explore and understand data can take a data science course.
