Why do I need to learn about machine learning?
Machine learning has solved many important difficult problems recently. A few of them include speech recognition, speech synthesis, image recognition, autonomous driving and chat bots.
Nowadays a key skill of software developer is the ability to use machine learning algorithms solve real-world problems.
What can I do after finishing learning about applied machine learning ?
You will be to create software that could recognize car plate number from an image, identify probability of breast cancer for a patient.
That sounds useful! What should I do now?
At the same time, please read
– this Aurelien Geron (2022). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. O’Reilly Media book and
– this Brett Lantz (2019). Machine Learning with R – Expert Techniques for Predictive Modeling. Packt Publishing book, and
– this Michael A. Nielsen (2015). Neural Networks and Deep Learning. Determination Press book.
After that please read
– this Tom M. Mitchell (1997). Machine Learning. McGraw-Hill Education book, and
– this Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer book.
- Artificial Intelligence.
- Machine Learning.
- Deep Learning.
- Linear Regression: Y = θᵀX + Ε.
- Cost Function measures how good/bad your model is.
- Mean Square Error (MSE) measures the average of the squares of the errors.
- Gradient Descent, Learning Rate.
- Batch Gradient Descent.
- The R-Squared Test measures the proportion of the total variance in the output (y) that can be explained by the variation in x. It can be used to evaluate how good a “fit” some model is on the given data.
- Stochastic Gradient Descent.
- Mini-Batch Gradient Descent.
- Overfitting: machine learning model gives accurate predictions for training data but not for new data.
- Regularization: Ridge Regression, Lasso Regression, Elastic Net, Early Stopping.
- Logistic Regression.
- Sigmoid Function.
- Binary Cross Entropy Loss Function, Log Loss Function.
- One Hot Encoding.
- The Softmax Function takes an N-dimensional vector of arbitrary real values and produces another N-dimensional vector with real values in the range (0, 1) that add up to 1.0.
- Softmax Regression.
- Support Vector Machines.
- Decision Trees.
- K-Nearest Neighbors.
- McCulloch-Pitts Neuron.
- Linear Threshold Unit with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T.
- Activation Functions: Sigmoid, Hyperbolic Tangent, Rectified Linear Unit (ReLU).
- Artificial Neural Networks.
- Gradient Descent Optimization Algorithms: Momentum, Adagrad, Adadelta, RMSprop, Adam.
- Regularization: Dropout.
- Principal Component Analysis.
- User-Based Collaborative Filtering.
- Item-based Collaborative Filtering.
- Matrix Factorization.
- The Joint Probability Table.
- Bayesian Networks.
- Naive Bayes Inference.
After finishing learning about machine learning please click Topic 23 – Introduction to Computer Vision to continue.