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Data Analytics with Python (Course Introduction)

Learn Python in 10 minutes

Numpy and Matplotlib Tutorial

Pandas Tutorial - Python

Data Preprocessing Steps for Machine Learning & Data analytics

Machine Learning for Data Analytics Intro

SkLearn Linear Regression (Housing Prices Example)

Sklearn Random Forest Classifier (Digit Recognition Example)

SKlearn PCA, SVD Dimensionality Reduction

Machine Learning with Text - Count Vectorizer Sklearn (Spam Filtering example Part 1 )

Machine Learning with Text - TFIDF Vectorizer MultinomialNB Sklearn (Spam Filtering example Part 2)

Bias Variance Trade off

Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax)

Perceptron and Gradient Descent Algorithm - Scikit learn

Neural Networks and Backpropogation Scikit learn

Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting

Scikit Learn Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting

Twitter live sentiment Analysis Tutorial in Python - Tweepy and TextBlob

Description:

Explore data analytics with Python in this comprehensive 2.5-hour tutorial series. Learn to analyze numerical, textual, and image data using Python 2.7, Numpy, Pandas, Matplotlib, and Sklearn. Begin with data exploration and preprocessing basics, then delve into machine learning concepts and feature extraction. Apply regression and Random Forest classification to image data using Scikit-learn. Discover text data analysis through spam classification examples. Study perceptrons, Artificial Neural Networks (ANNs), and activation functions. Develop a sentiment analyzer for tweets using Tweepy. Master data visualization, make predictions, and gain practical experience with Kaggle competitions. Cover essential topics including linear regression, dimensionality reduction, ensemble learning, and more. By the end, confidently analyze and visualize datasets, making you ready to tackle real-world data analytics challenges.

Data Analytics with Python

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