What you'll learn
Machine learning is a data analysis method that is used to automate analytical model building. It is a subset of Artificial Intelligence (AI) and focuses on analyzing structures and patterns in data to allow reasoning, learning, and decision-making without human interactions. In simple terms, Machine learning will let the user feed large volumes of data to a computer algorithm and make the device process the data to offer recommendations that are data-driven and decisions that are only based on the data offered.
Course Content
Python & Advanced Python
- PyCharm, Language Fundamentals, Input-Output, Operators, Conditional Statements, Looping, Control Statements, Functions, Modules, String Manipulation, Lists, Tuple, Set, Dictionaries, File Operations, OOPS Concepts, Regular Expressions, Functional Programming, Map, Reduce, Filter, Iter tools, list comprehension
SQL
- Introduction to SQL, Installation, SQL queries for data retrieval, filtering, CRUD and aggregation. Joins and subqueries in SQL for merging datasets. Exploratory data analysis in SQL database.
EDA & Data Visualization
- Introduction to python for data science (Numpy, Pandas), Data Visualization (Matplotlib, Seaborn), Exploratory Data Analysis ( Handling missing data, Exploratory Data Analysis using Pandas)
Supervised Learning
- Regression (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Evaluation of regression models, L1 and L2 Regularization Techniques), Classification (K Nearest Neighbors (KNN), Naive Bayes Classifier, Decision Tree Algorithm, XGBoost, Random Forest Algorithm, Support Vector Machines (SVM) with L1 and L2 Regularization.
Unsupervised Learning
- Introduction to Clustering Algorithms, K-Means Clustering, Hierarchical Clustering, Elbow Method for optimal K in K-Means.
Natural Language Processing & Model Deployment
- Text processing basics, Sentiment analysis, Introduction to tokenization and stemming, Overview of pre-trained language models. Basics of deploying machine learning models , Capstone Project (Applying Python, SQL, and machine learning to a real-world problem)
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