Coding Ninja Data Scienece and Machine Learning

Leactures

• Lecture 11 - NumPy
• Lecture 12 - Pandas
• Lecture 13 - Plotting Grpahs
• Lecture 14 - Structured Query Language(SQL) - Basic
• Lecture 15 - Structured Query Language(SQL) - Advance
• Lecture 16 - Indexing And SQLite
• Lecture 19 - Web Scraping - BeautifulSoup
• Lecture 32 - Introduction To Machine Learning
• Lecture 33 - Linear Regression
• Lecture 33 - Multi Variable Regression
• Lecture 34 - Feature Scaling
• Lecture 35 - Logistic Regression
• Lecture 36 - Classification Measures
• Lecture 37 - Decision Tree 1
• Lecture 38 - Decision Tree 2
• Lecture 39 - Random Forest
• Lecture 40 - Naive Bayes
• Lecture 41 - K - Nearest Neighbours (K - NN)
• Lecture 42 - Support Vector Machine (SVM)
• Lecture 43 - Principal Component Analysis (PCA) - 1
• Lecture 44 - Principal Component Analysis (PCA) - 2
• Lecture 45 - Natural Language Processing (NLP) - 1
• Lecture 46 - Natural Language Processing (NLP) - 2
• Lecture 47 - Neural Networks - 1
• Lecture 48 - Neural Networks - 2
• Lecture 49 - TensorFlow
• Lecture 50 - Keras
• Lecture 51 - Convolution Neral Network - 1
• Lecture 52 - Convolution Neral Network - 2
• Lecture 53 - Recurrent Neural Network
• Lecture 54 - Long Short Term Memory
• Lecture 55 - Unsupervised Learning - 1
• Lecture 56 - Unsupervised Learning - 2

Projects

• Project: Gradient Descent
• Project: Logistic Regression
• Project: Text Classification
• Project: Twitter Sentiment Analysis

Revisit required

• L34.2 Complexity Analysis Of Normal Equation

Continue on

• L34.4 Learning Rate