Data Science Course in Bharuch
Data science is more than just a buzzword. Every company needs workers who can analyze data to come up with insightful conclusions. Data science is gaining in popularity and this trend doesn't appear to be slowing.
Data science is a diverse field that combines computer science, artificial intelligent, statistics, and math to analyze huge amounts of data. This provides businesses with valuable information. Data scientists can provide valuable insights by identifying patterns and trending. This allows them to make predictions about the future, historical events, and their causes. These visions can help with organizational efficiency, competitive edge, and rational decision-making. Join for our DTP Course in Surat.
Artificial Intelligence and Machine Learning Course in Bharuch
Students who take the Python for Artificial Intelligence & Machine Learning course will gain a solid understanding of Python programming, and its applications to AI and ML. Students will learn how to create AI/ML algorithm, work with popular frameworks and libraries, and create useful AI/ML solution in Python. Artificial intelligence (AI) is the process of teaching robots how to mimic human learning. This will be covered by artificial intelligence training. Sign up for the Artificial Intelligence Course Certification program to use AI in your most important business functions. You can check out our Ethical Hacking Course in Surat.
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
