Data Science & Machine Learning

Duration: 4 Months

About Data Science

Data science involves extracting insights and knowledge from complex and large datasets using a combination of statistics, programming, and domain expertise. It encompasses data collection, cleaning, exploration, and modeling, often employing machine learning techniques. The goal is to uncover patterns, make predictions, and drive informed decision-making across various fields, from business and healthcare to social sciences. Successful data science requires a blend of analytical skills, domain knowledge, and effective communication to translate findings into actionable solutions.

Course Curriculum:

  • Using Databases with Python
  • Object Oriented Python
  • Data Models
  • Relational SQL Databases
  • Visualisation
  • Introduction to NumPy
  • Pandas and Matplotlib etc.
  • Data Manipulation

About Machine Learning

Machine learning is a branch of artificial intelligence that enables computers to learn patterns from data. It involves creating algorithms that can improve their performance over time by adapting to new information. This approach is used for tasks like image recognition, language translation, and recommendation systems. Machine learning models are trained on data to make predictions or decisions without being explicitly programmed. Common techniques include supervised learning, unsupervised learning, and reinforcement learning.

Course Curriculum:

  • Introduction to Machine Learning with Python
  • Supervised Learning - I
  • Dimensionality Reduction
  • Supervised Learning - II
  • Unsupervised Learning
  • Define Data Science
  • Data Science Fundamentals
  • Data Extraction
  • Wrangling
  • Visualization
  • Data Analysis Pipeline
  • What is Data Extraction?
  • Types of Data
  • Raw and Processed Data
  • Exploratory Data
  • Analysis Visualization of Data
  • Explore Algorithms like: Regression, Clustering, Decision Tree, Random forest, Naive Bayes, etc.
  • Elements of machine learning algorithm like:Parameters,Hyper parameters, Loss function, Optimization.
  • Association Rules Mining and Recommendation Systems
  • Reinforcement Learning
  • Time Series Analysis
  • Model Selection and Boosting
  • Supervised Learning -- Project for your profile
  • Unsupervised Learning-II- Project for your profile
Data Science and Machine Learning