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?

Please audit
– this Machine Learning Specialization (Coursera) courses and
– this Applied Machine Learning in Python (Coursera) course.

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 watch
– this MIT 6.034 – Artificial Intelligence, Fall 2010 course (Readings).

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.

Terminology Review:

• 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.
• Perceptron.
• Activation Functions: Sigmoid, Hyperbolic Tangent, Rectified Linear Unit (ReLU).
• Artificial Neural Networks.
• Backpropagation.
• Gradient Descent Optimization Algorithms: Momentum, Adagrad, Adadelta, RMSprop, Adam.
• Regularization: Dropout.
• K-Means.
• 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.