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Machine Learning Course Introduction

Machine learning is a field of study that looks at using computational algorithms to turn empirical data into usable models. The machine learning field grew out of traditional statistics and artificial intelligences communities. From the efforts of mega corporations such as Google, Microsoft, Facebook, Amazon, and so on, machine learning has become one of the hottest computational science topics in the last decade. Through their business processes immense amounts of data have been and will be collected. This has provided an opportunity to re-invigorate the statistical and computational approaches to autogenerate useful models from data.

Machine learning algorithms can be used to (a) gather understanding of the cyber phenomenon that produced the data under study, (b) abstract the understanding of underlying phenomena in the form of a model, (c) predict future values of a phenomena using the above-generated model, and (d) detect anomalous behavior exhibited by a phenomenon under observation. There are several open-source implementations of machine learning algorithms that can be used with either application programming interface (API) calls or nonprogrammatic applications

Machine Learning Online Training Course Content

  • 1. INTRODUCTION TO MACHINE LEARNING
  • 1.1 Need of Machine Learning
  • 1.2 Introduction to Machine Learning
  • 1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
  • 2. SUPERVISED LEARNING & LINEAR REGRESSION
  • 2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
  • 2.2 Introduction to regression
  • 2.3 Simple linear regression
  • 2.4 Multiple linear regression and assumptions in linear regression
  • 2.5 Math behind linear regression
  • 3. CLASSIFICATION & LOGISTIC REGRESSION
  • 3.1 Introduction to classification
  • 3.2 Linear regression vs logistic regression
  • 3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
  • 4. DECISION TREE & RANDOM FOREST
  • 4.1 Introduction to tree-based classification
  • 4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
  • 4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
  • 4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
  • 5. NAÏVE BAYES & SUPPORT VECTOR MACHINES (SELF-PACED)
  • 5.1 Introduction to probabilistic classifiers
  • 5.2 Understanding Naïve Bayes and math behind the Bayes theorem
  • 5.3 Understanding a support vector machine (SVM)
  • 5.4 Kernel functions in SVM and math behind SVM
  • 6. UNSUPERVISED LEARNING
  • 6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
  • 6.2 Introduction to k-means clustering
  • 6.3 Math behind k-means
  • 6.4 Dimensionality reduction with PCA
  • 7. NATURAL LANGUAGE PROCESSING & TEXT MINING (SELF-PACED)
  • 7.1 Introduction to Natural Language Processing (NLP)
  • 7.2 Introduction to text mining
  • 7.3 Importance and applications of text mining
  • 7.4 How NPL works with text mining
  • 7.5 Writing and reading to word files
  • 7.6 Language Toolkit (NLTK) environment
  • 7.7 Text mining: Its cleaning, pre-processing, and text classification
  • 8. INTRODUCTION TO DEEP LEARNING
  • 8.1 Introduction to Deep Learning with neural networks
  • 8.2 Biological neural networks vs artificial neural networks
  • 8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
  • 9. TIME SERIES ANALYSIS (SELF-PACED)
  • 9.1 What is time series? Its techniques and applications
  • 9.2 Time series components
  • 9.3 Moving average, smoothing techniques, and exponential smoothing
  • 9.4 Univariate time series models
  • 9.5 Multivariate time series analysis
  • 9.6 ARIMA model and time series in Python
  • 9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis

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