Data Science And Machine Learning

The course in data science and machine learning involves the study of techniques and tools for analyzing and interpreting large sets of data. It covers topics such as statistical analysis, machine learning algorithms, data visualization, and data manipulation.

The scope of the course includes developing skills for extracting insights from data and making predictions using advanced computational methods. The course is highly relevant in the current age of big data and has a wide range of applications in various fields such as healthcare, finance, marketing, and more.

Jobs You Can Apply:

After completing a course in data science and machine learning, one can apply for a variety of job roles, such as: Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, Data Engineer, AI/ML Researcher, Data Visualization Specialist, Big Data Analyst, Predictive Analytics Specialist, Quantitative Analyst.

What You Will Learn:

• Python Programming For Data Science
• Python Variables
• Collections Of Variables
• Looping Statements
• Conditional Statements
• Functions
• Oops Concepts
• File Handling
• Debugging
• Data Structures And Algorithm
• Os Module
• Analyzing Data With Python
• Relational Databases & Nosol
• Big-data Databases
• Statistics & Probability
• Importing Datasets
• Cleaning And Preparing The Data
• Summarizing The Data Frame
• Visualizing Data With Python
• Matplotlib
• Line Plots
• Basic Visualization Tools
• Area Plots. Histograms, Bar Charts
• Specialized Visualization Tools
• Pie Charts, Box, Scatter & Bubble Plots
• Deep Learning
• Introduction To Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Tensor Flow2
• Tableau
• Bringing In Data
• Connecting To Data
• Visualizing Data
• Analyzing Data
• Creating Dashboards
• Publishing And Sharing
• Advanced Visual Analytics
• Machine Learning
• Supervised Learning
• Classification And Regression
• Linear Models
• Naive Bayes Classifers
• Decision Trees
• Unsupervised Learning
• Principal Component Analysis (pca)
• K-means Clustering
• Recommender Systems
• User-based & Item-based Collaborative Filtering
• Binning, Discretization, Linear Models & Trees
• Model Evaluation And Ilmprovement
• Cross-validation In Scikit-learn
• Algorithm Chains And Pipelines
• Prototype To Production
• Nlp (natural Language Processing)
• Types Of Data Represented As Strings
• Representing Text Data As A Bag Of Words
• Stopwords
• Rescaling The Data With Tf-idf
• Investigating Model Coefficients
• Advanced Tokenization, Stemming
• Lemmatization
• Machine Learning And Deep Learning
• Natural Language Applications
• Big Data
• Hadoop Overview
• Spark Overview
• Capstone Project
• Capstone Project