Data Science Course in Ahmedabad
Data science is a multidisciplinary field that analyzes large volumes of data using mathematics, statistics, AI, and computer engineering to extract meaningful insights for businesses. By uncovering patterns and trends, data scientists can answer critical questions about past events, reasons behind them, future predictions, and potential actions to take. These insights enable businesses to make informed decisions, optimize operations, and gain a competitive edge.
Artificial Intelligence and Machine Learning Course in Ahmedabad
Data Science & Python for Artificial Intelligence and Machine Learning course is aimed to give students a solid foundation in Python programming as well as its applications in AI and ML. We offer an Artificial Intelligence Course in Surat by professional experts. This course will teach students how to construct AI/ML algorithms, interact with popular libraries and frameworks, and create actual AI/ML solutions in 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
C, C++ And
Python for Data Science
- 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
Mathematics of Data Science
- 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
SQL for Data
Science
- 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
NumPy and
Pandas
- What is Pandas?
- What is NumPy?
- Difference between Pandas and NumPy
- Numpy And Pandas Operations for Data Science
Data Analysis
Process
- 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
Data Visualization
- Matplotlib & Seaborn for Data Science
- Charts, Pie charts, Scatter and bubble charts
- Bar charts, Column charts, Line charts, Maps
Supervised Learning
Algorithms
-
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
Unsupervised
Learning Algorithm
-
Clustering
- K - Means clustering
- Hierarchal clustering
- DBSCAN
- Recommender System
- Association Rule Learing
- Apriori algorithm
- Small Projects
Deep learning -
Computer Vision and Image Analysis
- 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
NLP
- 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
Reinforcement
Learning algorithm
- Markov Decision Process
- Thompos Sampling
- Upper Confidence Bound
Dimensionality
Reduction
- Principle Component Analysis
- Linear Discrimination Analysis
- Kernel PCA
Model Selections &
Ensembled Techniques , Regularization Techniques
- Random Train/Test Split
- Resampling
- Lasso Regression
- Ridge Regression
- Probabilistic Model Selection
- Boosting and Bagging
- Random Forest
- XGBM
Live Projects End
to End - Final Project
- Module Assignments
- End to End project Description with deployment using Python
