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.
Overview
Syllabus
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.
Taught by
The Semicolon
