All Topics of Machine Learning You Should Know
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
#1: Introduction to Machine Learning
What is Machine Learning?
Why do we need Machine Learning?
Which Types of Problems does Machine Learning solve?
Types of Data you deal with?
#2: Supervised Learning?
What is Supervised Learning?
Classification
Regression
Classification Algorithms
Regression Algorithms
Model Evaluation Metrics for Classification and Regression
#3: Unsupervised Learning
What is Unsupervised Learning?
Preprocessing and Scaling Datasets
Dimensionality Reduction
Feature Extraction
Manifold Learning
Clustering
Clustering Algorithms
#4: Feature Engineering
Categorical Features
One-hot-encoding
Binning and Discretization
Interaction and Polynomials
Univariate Nonlinear Transformations
Linear Models and Trees
Feature Selection
#5: Model Evaluation
Overfitting and Underfitting
Cross-Validation
Grid Search
Evaluation Metrics
Model Selection
Hyperparameter Tuning
#6: Working with Text Data
Types of Textual Data
Analyzing Sentiments
Bag of words
Stopwords
Tf-Idf
Tokenization
Stemming
Lemmatization
Topic Modelling
Document Clustering
#7: Pipelines
Parameter Selection
Building Pipelines
Using Pipelines in Grid Search
