About Data Science, Artificial Intelligence and Machine Learning Course in Vadodara
Data Science & Python for Artificial Intelligence and Machine Learning course is designed to provide students with a solid foundation in Python programming and its applications in the field of AI and ML. This course aims to equip students with the necessary skills to implement AI/ML algorithms, work with popular libraries and frameworks, and develop practical AI/ML solutions using Python. Get Python Training in Vadodara at rednWhite institute.
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
