Data Science and Machine Learning

Course Content

What You'll Learn

Python Libraries: Master NumPy for math and Pandas for data manipulation.
Supervised Learning: Build Regression and Classification models (KNN, Trees, SVM).
Unsupervised Learning: Explore Clustering (K-Means) and PCA for data reduction.
Data Visualization: Create stunning charts using Matplotlib and Seaborn.
Real-World Practice: Complete a hands-on Kaggle heart attack prediction project.
Performance Metrics: Evaluate models using Cross-Validation and error analysis.
Complex Systems: Learn to build Recommender Systems and Neural Network foundations
AI Fundamentals: Understand the core logic behind Machine Learning and AI.

Requirements

Python Basics: A simple understanding of variables, loops, and functions.
Tech Literacy: Ability to install software and navigate your computer.
Basic Hardware: Any PC, Mac, or Linux machine capable of running a browser.
Software: Usage of free tools like Anaconda and Jupyter Notebooks.
Commitment: Desire to finish lectures in order and tackle practical challenges.
No Advanced Math: No PhD required; we explain the math in plain English.
Ambition: A passion to join the high-growth field of Data Science.

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Data Science and Machine Learning

₹599

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: Data Frame Structures in Pandas Library 4 lectures

Module 11: Element Selection Operations in Data Frame Structures 6 lectures

Module 12: Structural Operations on Pandas Data Frame 5 lectures

Module 13: Multi-Indexed Data Frame Structures 3 lectures

Module 14: Structural Concatenation Operations in Pandas Data Frame 6 lectures

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

Module 16: Pivot Tables in Pandas Library 2 lectures

Module 17: File Operations in Pandas Library 5 lectures

Module 19: Introduction to Data Visualization with Python 1 lectures

Module 20: Matplotlib 9 lectures

Module 21: Seaborn 7 lectures

Module 22: Geoplotlib 4 lectures

Module 23: First Contact with Machine Learning 2 lectures

Module 24: Evaluation Metrics in Machine Learning 4 lectures

Module 25: Supervised Learning with Machine Learning 1 lectures

Module 26: Linear Regression Algorithm in Machine Learning 5 lectures

Module 27: Bias Variance Trade-Off in Machine Learning 1 lectures

Module 28: Logistic Regression Algorithm in Machine Learning 6 lectures

Module 29: K-fold Cross-Validation in Machine Learning 2 lectures

Module 30: K Nearest Neighbors Algorithm in Machine Learning 4 lectures

Module 31: Hyperparameter Optimization 2 lectures

Module 32: Decision Tree Algorithm in Machine Learning 6 lectures

Module 33: Random Forest Algorithm in Machine Learning 3 lectures

Module 34: Support Vector Machine Algorithm in Machine Learning 5 lectures

Module 35: Unsupervised Learning with Machine Learning 1 lectures

Module 36: K Means Clustering Algorithm in Machine Learning 5 lectures

Module 37: Hierarchical Clustering Algorithm in machine learning data science 3 lectures

Module 38: Principal Component Analysis (PCA) in Machine Learning 4 lectures

Module 39: Recommender System Algorithm in Machine Learning 2 lectures

Module 40: First Contact with Kaggle 3 lectures

Module 41: Competition Section on Kaggle 2 lectures

Module 42: Dataset Section on Kaggle 1 lectures

Module 43: Code Section on Kaggle 3 lectures

Module 44: Discussion Section on Kaggle 1 lectures

Module 45: Other Most Used Options on Kaggle 3 lectures

Module 46: Details on Kaggle 4 lectures

Module 47: Introduction to Machine Learning with Real Hearth Attack Prediction Project 4 lectures

Module 48: First Organization 3 lectures

Module 49: Preparation For Exploratory Data Analysis (EDA) in Data Science 4 lectures

Module 50: Exploratory Data Analysis (EDA) - Uni-variate Analysis 5 lectures

Module 51: Exploratory Data Analysis (EDA) - Bi-variate Analysis 14 lectures

Module 52: Preparation for Modelling in Machine Learning 11 lectures

Module 53: Modelling for Machine Learning 8 lectures

Module 54: Conclusion 1 lectures

What You'll Learn

Requirements