Description
Curriculum
Instructor
Machine Learning is a subset of artificial intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that enable computers to make predictions or decisions based on data, which is broadly defined as the capability of a machine to imitate intelligent human behavior.
- 3 Sections
- 66 Lessons
- 10 Weeks
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- MACHINE LEARNING ASSOCIATE32
- 1.1WHAT IS MACHINE LEARNING
- 1.2MACHINE LEARNING VS ARTIFICIAL INTELLIGENCE
- 1.3STATISTICAL MODELLING OF MACHINE LEARNING.
- 1.4APPLICATIONS OF MACHINE LEARNING.
- 1.5POPULAR ML ALGORITHMS
- 1.6CLUSTERING MODELS
- 1.7CLASSIFICATION VS REGRESSION MODELS
- 1.8SUPERVISED VS UNSUPERVISED ML MODELS
- 1.9CHOICE OF ML MODEL
- 1.10REGRESSION LINE
- 1.11LINE OF BEST FIT
- 1.12ASSUMPTIONS OF SIMPLE LINEAR REGRESSION
- 1.13READING AND UNDERSTANDING THE DATA
- 1.14HYPOTHESIS TESTING IN LINEAR REGRESSION
- 1.15BUILDING A LINEAR MODEL
- 1.16RESIDUAL ANALYSIS AND PREDICTIONS
- 1.17LINEAR REGRESSION USING SKLEARN
- 1.18SIMPLE LINEAR REGRESSION VS MULTIPLE LINEAR REGRESSION.
- 1.19MULTICOLLINEARITY
- 1.20DEALING WITH CATEGORICAL VARIABLES
- 1.21MODEL ASSESMENT AND COMPARISON
- 1.22FEATURE SELECTION
- 1.23INTRODUCTION: UNIVARIATE LOGISTIC REGRESSION
- 1.24BINARY CLASSIFICATION
- 1.25SIGMOID CURVE
- 1.26FINDING THE BEST FIT SIGMOID CURVE
- 1.27MULTIVARIATE LOGISTIC REGRESSION
- 1.28DATA CLEANING AND PREPARATION
- 1.29BUILDING YOUR FIRST MODEL
- 1.30FEATURE ELIMINATION USING RFE
- 1.31CONFUSION MATRIX AND ACCURACY
- 1.32MANUAL FEATURE ELIMINATION
- MACHINE LEARNING ASSOCIATE (PART 2)19
- 2.1METRICS BEYOND ACCURACY: SENSITIVITY & SPECIFICITY
- 2.2FINDING THE OPTIMAL THRESHOLD USING ROC CURVE
- 2.3METRICS BEYOND ACCURACY: PRECISION& RECALL
- 2.4INTRODUCTION TO KNN
- 2.5HOW KNN WORKS.
- 2.6PROS AND CONS OF KNN
- 2.7APPLICATION OF KNN
- 2.8KNN MODEL BUILDING
- 2.9EVALUATING A KNN MODEL.
- 2.10UNSUPERVISED LEARNING: CLUSTERING (INTRODUCTION)
- 2.11UNDERSTANDING CLUSTERING MODELS
- 2.12PRACTICAL EXAMPLE OF CLUSTERING – CUSTOMER SEGMENTATION
- 2.13K MEANS CLUSTERING (INTRODUCTION) .
- 2.14PRACTICAL CONSIDERATION IN K MEANS ALGORITHM
- 2.15CLUSTER TENDENCY
- 2.16K MEANS IN PYTHON CASE: IRIS DATASET CLUSTERING
- 2.17HIERARCHICAL CLUSTERING ALGORITHM
- 2.18UNSUPERVISED LEARNING: PRINCIPLE COMPONENT ANALYSIS (PCA)
- 2.19PCA: IRIS DATASET
- ADVANCED MACHINE LEARNING15
- 3.1INTRODUCTION.
- 3.2DECISION TREE ON WINE DATASET (Python Implementation)
- 3.3CONCEPT OF HOMOGENEITY
- 3.4GINI INDEX
- 3.5ENTROPY AND INFORMATION GAIN
- 3.6SPLITTING BY R-SQUARED
- 3.7DECISION TREE HYPERPARAMETER TUNING
- 3.8TREE TRUNCATION
- 3.9RANDOM FOREST ENSEMBLE BAGGING TECHNIQUE
- 3.10BAYES THEOREM AND ALGORITHM BUILDING BLOCKS
- 3.11NAIVE BAYES: TEXT CLASSIFICATION HAM VS SPAM CASESTUDY.
- 3.12SUPPORT VECTOR MACHINE
- 3.13BOOSTING: INTRODUCTION, ADABOOST, GRADIENT BOOSTING, XGBOOST
- 3.14ARTIFICIAL NEURAL NETWORKS.
- 3.15ADVANCED ML CONCEPTS.
tony
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