This course on Data Analytics with Python aims to teach learners the basics of data analytics using Python 2.7. By the end of the course, students will be able to analyze and visualize datasets, make predictions on data, and apply machine learning algorithms such as regression, random forest classifier, perceptrons, and artificial neural networks. The course covers data preprocessing, machine learning for data analytics, text data analysis, sentiment analysis on tweets, and the implementation of various machine learning algorithms using libraries like Numpy, Pandas, Matplotlib, and Scikit-learn. The intended audience for this course includes beginners interested in data analytics, Python programming, and machine learning.
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
