Fundamentals of Data Science - An Introductory Guide to Data Science and Machine Learning

0% completed

All LessonsFree Lessons (4)

What is Data Science ?

Data Science vs. Data Analysis vs. Data Engineering Descriptive and Predictive Analytics Data Science Life Cycle Structured vs. Semi-Structured vs. Unstructured Data

Applications of Data Science

Applications in Healthcare and Recommender Systems Image Analysis

Overview of Libraries

Beautiful Soup (Scraping Data from Simple HTML)Beautiful Soup (Scraping Data from Html Table)Scrapy Numpy Basics Numpy Array Creation Numpy Array Manipulation Sorting Numpy Arrays Basic Statistics on Numpy Arrays Broadcasting in Numpy Arrays Pandas Spacy Part 1 Spacy Part 2 Seaborn

Probability and Statistics

Probability Statistics Joint Probability Conditional Probability and Bayes Theorem Measures of Locations Measures of Variability Probability Distributions (Binomial and Bernoulli Distributions)Gaussian Distribution Poisson Distribution Skewness and Kurtosis Sampling Methods Key Concepts in Statistics Statistical Hypothesis Testing

Machine Learning Part-1

Machine Learning and its Types Deep Learning and Recommender Systems What is Regression ?Univariate Linear Regression Multivariate Linear Regression Feature Scaling Linear Regression in Scikit Learn Regularization (Lasso, Ridge, and ElasticNet Regression)Support Vector Regression Nearest Neighbour Regression Decision Tree Regression Feature Engineering and Categorical Variables Encoding Numerical Variables Transformation Feature Selection (Filter Methods)Feature Selection (Wrapper Methods)Feature Selection (Intrinsic Methods)Model Evaluation Measures (Explained Variance Score, MAE, MSE)Model Evaluation Measures (Median Absolute Error, R^2 Score)Dummy Regressors Cross Validation Case Study: House Prices Prediction using Advanced Regression

Machine Learning Part-2

Types of Classification Problems Logistic Regression Support Vector Machines Decision Trees Naive bayes Part-1 Naive bayes Part-2 K-Nearest Neighbors Ensemble Learning Part 1 Ensemble Learning Part 2 XGBoost, Light GBM and CatBoost Learning Curves Model Evaluation Part 1 Model Evaluation Part 2 Dummy Estimators and Handling Imbalance Class Problem Hyper-Parameter Optimization and Kaggle Competition

Machine Learning Part-3

Unsupervised Learning K-Means Clustering Hierarchical Clustering DBSCAN Clustering and Customer Segmentation Apriori Algorithm and Association Rules Principal Component Analysis for Dimensionality Reduction Semi-Supervised Learning Techniques

Deep Learning

What is Deep Learning?Neural Networks Feedforward Neural Networks Backpropagation Part 1 Backpropagation Part 2 Convolutional Neural Network Recurrent Neural Network LSTM Networks

Machine Learning Tools and Libraries

Automated Machine Learning Pandas Profiling and PyCaret RAPIDS (Using GPU for Fast Computations)

Big Data Tools and Technologies

What is Big Data ?Hadoop Ecosystem Map Reduce Framework Apache Spark and it's Components

Where to go next ?

Starting Career on Kaggle (Tips)Recommended Courses from Educative References and Acknowledgements

Fundamentals of Data Science

Machine Learning Fundamentals