Machine Learning Course Introduction

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