Data Science and Machine Learning

Top Pick

Master the Intelligence Age: Data Science and Predictive Analytics In a world defined by information, the power to decode complex datasets and forecast future outcomes is the most vital skill of the modern era. This course serves as your definitive guide to navigating the full Data Science and Machine Learning landscape through the lens of Python. Whether your goal is to streamline intricate workflows, uncover non-obvious correlations, or architect sophisticated AI solutions, this program delivers the rigorous training necessary to turn chaotic data into a strategic asset. From the initial stages of data synthesis to the deployment of high-level algorithms, you will gain hands-on experience with the tools that define today’s tech industry. You’ll move beyond mere theory, learning how to engineer features, optimize models, and communicate results that drive real-world impact. Prepare to transition from a data consumer to a data architect, equipped with the technical depth and analytical clarity to lead in an increasingly automated world.

53 Modules

226 Lectures

40H

₹399

Course Content

What You'll Learn

Core Libraries: Gain proficiency in NumPy and Pandas for high-speed computation and data handling.
Supervised Models: Architect predictive systems using Regression, Trees, and SVM.
Pattern Discovery: Use Clustering and PCA to find hidden structures in complex data.
Visual Storytelling: Design impactful graphics with Matplotlib and Seaborn.
Applied Analytics: Tackle a real-world Kaggle challenge to predict health outcomes.
Model Assessment: Master error analysis and cross-validation to ensure model reliability.
Advanced Logic: Explore the foundations of Neural Networks and Recommendation Engines.
AI Principles: Internalize the fundamental theory driving modern Artificial Intelligence.

Requirements

Python Essentials: Basic comfort with loops, functions, and variables.
Computer Literacy: Ability to navigate your OS and manage software installations.
Standard Hardware: Any modern PC or Mac capable of web browsing.
Tooling: Use of open-source platforms like Jupyter and Anaconda.
Logic over Theory: No advanced math background needed; we simplify the complex concepts.
Growth Mindset: A drive to complete projects and enter the data science industry.

Data Science and Machine Learning

₹399

Course Content

What You'll Learn

Requirements

Course Certificate

Why get certified?

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Verified Skills

Data Science and Machine Learning

₹399

Course Content

Module 1: Introduction 1 lectures

Module 2: Installation 2 lectures

Module 3: NumPy Library Introduction 2 lectures

Module 4: Creating NumPy Array in Python 9 lectures

Module 5: Functions in the NumPy Library 7 lectures

Module 6: Indexing, Slicing, and Assigning NumPy Arrays 9 lectures

Module 7: Operations in Numpy Library 4 lectures

Module 8: Pandas Library Introduction 1 lectures

Module 9: Series Structures in the Pandas Library 7 lectures

Module 10: DataFrame Structures in Pandas Library 4 lectures

Module 11: Element Selection Operations in DataFrame Structures 6 lectures

Module 12: Structural Operations on Pandas DataFrame 5 lectures

Module 13: Multi-Indexed DataFrame Structures 3 lectures

Module 14: Structural Concatenation Operations in Pandas DataFrame 6 lectures

Module 15: Functions That Can Be Applied on a DataFrame 9 lectures

Module 16: Pivot Tables in Pandas Library 2 lectures

Module 17: File Operations in Pandas Library 5 lectures

Module 18: Introduction to Data Visualization with Python 1 lectures

Module 19: Matplotlib 9 lectures

Module 20: Seaborn 7 lectures

Module 21: Geoplotlib 4 lectures

Module 22: First Contact with Machine Learning 2 lectures

Module 23: Evaluation Metrics in Machine Learning 4 lectures

Module 24: Supervised Learning with Machine Learning 1 lectures

Module 25: Linear Regression Algorithm in Machine Learning A-Z 5 lectures

Module 26: Bias Variance Trade-Off in Machine Learning 1 lectures

Module 27: Logistic Regression Algorithm in Machine Learning A-Z 6 lectures

Module 28: K-fold Cross-Validation in Machine Learning A-Z 2 lectures

Module 29: K Nearest Neighbors Algorithm in Machine Learning A-Z 4 lectures

Module 30: Hyperparameter Optimization 2 lectures

Module 31: Decision Tree Algorithm in Machine Learning A-Z 6 lectures

Module 32: Random Forest Algorithm in Machine Learning A-Z 3 lectures

Module 33: Support Vector Machine Algorithm in Machine Learning A-Z 5 lectures

Module 34: Unsupervised Learning with Machine Learning 1 lectures

Module 35: K Means Clustering Algorithm in Machine Learning A-Z 5 lectures

Module 36: Hierarchical Clustering Algorithm in machine learning data science 3 lectures

Module 37: Principal Component Analysis (PCA) in Machine Learning A-Z 4 lectures

Module 38: Recommender System Algorithm in Machine Learning A-Z 2 lectures

Module 39: First Contact with Kaggle 3 lectures

Module 40: Competition Section on Kaggle 2 lectures

Module 41: Dataset Section on Kaggle 1 lectures

Module 42: Code Section on Kaggle 3 lectures

Module 43: Discussion Section on Kaggle 1 lectures

Module 44: Other Most Used Options on Kaggle 3 lectures

Module 45: Details on Kaggle 4 lectures

Module 46: Introduction to Machine Learning with Real Hearth Attack Prediction Project 4 lectures

Module 47: First Organization 3 lectures

Module 48: Preparation For Exploratory Data Analysis (EDA) in Data Science 4 lectures

Module 49: Exploratory Data Analysis (EDA) - Uni-variate Analysis 5 lectures

Module 50: Exploratory Data Analysis (EDA) - Bi-variate Analysis 14 lectures

Module 51: Preparation for Modelling in Machine Learning 11 lectures

Module 52: Modelling for Machine Learning 8 lectures

Module 53: Conclusion 1 lectures

What You'll Learn

Requirements

Course Certificate

Why get certified?

Shareable on LinkedIn

Career Growth

Verified Skills