Data Science Course in Gandhinagar
Data science is now more than a slogan. Employees who can analyze data and come to insightful conclusions for the company are now essential to any successful business. This is certainly a major factor in data science's current surge in popularity, and it doesn't appear to be slowing down.
In order to analyze massive quantities of data and provide businesses with valuable analysis, data science is an area of study that combines computer engineering, statistics, artificial intelligence, and mathematics. Data scientists may be able to offer crucial insights into historical occurrences, their causes, potential future courses of action, and future predictions by spotting patterns and trends. Businesses can gain a competitive edge, optimize operations, and make logical decisions thanks to these views. Join rednwhite for DTP Course in Gandhinagar. Visit our website to find out the right course for your career.
Artificial Intelligence and Machine Learning Course in Gandhinagar
A thorough knowledge of Python programming and its applications in the fields of AI and ML will be received by students took classes in the Python for Artificial Intelligence and Machine Learning course. Throughout this course, students will gain knowledge on how to create AI/ML algorithms, collaborate with popular libraries and frameworks, and create useful AI/ML solutions in Python. The process of teaching robots to mimic human learning is known as artificial intelligence (AI), and it will be covered in artificial intelligence training in Gandhinagar. By signing up for the Artificial Intelligence Course certification program, you can use AI to automate your most crucial business processes. We also offer Python Training in Ahmedabad at Rednwhite.
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
