Data Science, Artificial Intelligence and Machine Learning Course in Navsari

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Data Science Course in Navsari

Data science has become more than just a trendy term. Every business now requires employees that can evaluate data and draw valuable conclusions for the organization. This is indeed one of the key causes of Data Science's recent rise to popularity, and it shows no signs of slowing down.

Data science is a multidisciplinary field that uses mathematics, statistics, artificial intelligence, and computer engineering to analyze vast volumes of data in order to derive useful insights for companies. By identifying patterns and trends, data scientists may provide important answers to questions about past events, the causes of them, future predictions, and possible course of action. These perceptions aid businesses in rational decision-making, operational optimization, and competitive advantage.

Artificial Intelligence and Machine Learning Course in Navsari

Students who take the Python for Artificial Intelligence and Machine Learning course will leave with a firm understanding of Python programming and its uses in the disciplines of AI and ML. Students will learn how to build AI/ML algorithms, work with well-known libraries and frameworks, and develop practical AI/ML solutions in Python during this course. You will learn about artificial intelligence (AI) throughout the artificial intelligence training in Bangalore, which is the process of teaching robots to emulate human learning. Utilize AI to automate your most important business activities by enrolling in the Artificial Intelligence Course certification program.

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