Machine Learning Tutorial | Beginner to Advanced Level.
I am writing a series on Machine Learning. It will take more than 3 months for me. Because I write articles only on weekends.
Machine Learning
1. Machine Learning Basics
1.1 Applications of Machine Learning
1.2 Why Machine Learning is the Future
1.3 Installing Python on Mac, Linux & Windows)
1.4 Installing Anaconda on Mac, Linux & Windows
2. Data Preprocessing
2.1 Get the dataset
2.2 Importing the Libraries
2.3 Importing the Dataset
2.4 Missing Data
2.5 Categorical Data
2.6 Splitting the Dataset into the Training set and Test set
2.7 Feature Scaling
3. Regression
3.1 Simple Linear Regression
3.2 Multiple Linear Regression
3.3 Polynomial Regression
3.4 Support Vector Regression (SVR)
3.5 Decision Tree Regression
3.6 Random Forest Regression
3.7 Evaluating Regression Models Performance
4. Classification
4.1 Logistic Regression
4.2 K-Nearest Neighbors (K-NN)
4.3 Support Vector Machine (SVM)
4.4 Kernel SVM
4.5 Naive Bayes
4.6 Decision Tree
4.7 Classification Random
4.8 Forest Classification
4.9 Evaluating Classification Models Performance
5. Clustering
5.1 K-Means Clustering
5.2 Hierarchical Clustering
5.3 Mean-Shift Clustering
5.4 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
5.5 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
6. Association Rule Learning
6.1 Useful Concepts of Association Rule Learning: Support, Confidence, Lift, Conviction
6.2 Apriori
6.3 Eclat
6.4 FP-Growth
7. Reinforcement Learning
7.1 Upper Confidence Bound (UCB)
7.2 Thompson Sampling
8. Natural Language Processing
8.1 What is Natural Language Processing
8.2 Natural Language Processing Intuition
8.3 How to get the dataset
8.4 Natural Language Processing in Python – Step 1
8.5 Natural Language Processing in Python – Step 2
8.6 Natural Language Processing in Python – Step 3
8.7 Natural Language Processing in Python – Step 4
9. Deep Learning
9.1 What is Deep Learning?
9.2 Artificial Neural Networks(ANN)
9.3 Convolutional Neural Networks(CNN)
9.4 Recurrent neural network(RNN)
9.5 Long short-term memory( LSTM)
9.6 Recursive Neural Tensor Network(RNTN)
10. Dimensionality Reduction
10.1 Principal Component Analysis (PCA)
10.2 Linear Discriminant Analysis (LDA)
10.3 Kernel PCA
11. Model Selection & Boosting
11.1 Model Selection
11.2 XGBoost
12. Applications
12.1 ChatBots
12.2 Question & Answer systems
13. Projects
13.1 Sentiment Analysis using RNTN
13.2 Image Classification with Convolutional Neural Networks
13.3 Handwritten Digit Recognition using Convolutional Neural Networks
About Author:
Currently, I am working as Data Scientist in Sentienz Solutions Pvt. Ltd. Sentienz is primarily a technology Solutions company supplying turn-key solutions based on the latest technologies in Cloud, Bigdata, Text Mining and Deep learning space.
