Data Science, Artificial Intelligence and Machine Learning Course in Bhavnagar

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Data Science Course in Bhavnagar

Data science is no longer simply a term. Employees that can analyze data and come to insightful insights for the company are increasingly essential to any firm. This is undoubtedly one of the main reasons for data science's recent and continued an increase in popularity. RednWhite Multimedia offers you data science course in Surat.

Data science is an integrated field that examines huge quantities of data to provide businesses with insightful information. It draws on computer science, statistics, artificial intelligence, and mathematics. Data scientists may offer significant insights into historical events, their causes, future projections, and possible paths of action by spotting patterns and trends. These perspectives help companies make logical decisions, optimize operations, and gain a competitive edge.

Artificial Intelligence and Machine Learning Course in Bhavnagar

A solid grasp of Python programming and its applications in the fields of AI and ML will be acquired by students enrolled 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 using 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 Bangalore. Become certified in the Artificial Intelligence Course and use AI to automate your most crucial business tasks. If you are interested, you can join artificial intelligence & machine learning course in Ahmedabad.

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
  • 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