Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Syllabus:

• Understanding Machine Learning Models
• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model
• More on Models
• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting
• Understanding Machine Learning Algorithms
• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Exploring Supervised Learning Algorithms
• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA
• Exploring Un-Supervised Learning Algorithms
• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means
• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering
• Exploring Hierarchical Clustering
• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering
• Understanding Dimensionality Reduction
• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)
• Understanding Hypothesis Testing
• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing
• Basics of Hypothesis
• Normalization
• Standard Normalization
• Parameters of Hypothesis Testing
• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test
• Overview Reinforcement Learning Algorithm
• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff
• Hands on Projects