3 Sections
66 Lessons
10 Weeks
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MACHINE LEARNING ASSOCIATE
32
1.1
WHAT IS MACHINE LEARNING
1.2
MACHINE LEARNING VS ARTIFICIAL INTELLIGENCE
1.3
STATISTICAL MODELLING OF MACHINE LEARNING.
1.4
APPLICATIONS OF MACHINE LEARNING.
1.5
POPULAR ML ALGORITHMS
1.6
CLUSTERING MODELS
1.7
CLASSIFICATION VS REGRESSION MODELS
1.8
SUPERVISED VS UNSUPERVISED ML MODELS
1.9
CHOICE OF ML MODEL
1.10
REGRESSION LINE
1.11
LINE OF BEST FIT
1.12
ASSUMPTIONS OF SIMPLE LINEAR REGRESSION
1.13
READING AND UNDERSTANDING THE DATA
1.14
HYPOTHESIS TESTING IN LINEAR REGRESSION
1.15
BUILDING A LINEAR MODEL
1.16
RESIDUAL ANALYSIS AND PREDICTIONS
1.17
LINEAR REGRESSION USING SKLEARN
1.18
SIMPLE LINEAR REGRESSION VS MULTIPLE LINEAR REGRESSION.
1.19
MULTICOLLINEARITY
1.20
DEALING WITH CATEGORICAL VARIABLES
1.21
MODEL ASSESMENT AND COMPARISON
1.22
FEATURE SELECTION
1.23
INTRODUCTION: UNIVARIATE LOGISTIC REGRESSION
1.24
BINARY CLASSIFICATION
1.25
SIGMOID CURVE
1.26
FINDING THE BEST FIT SIGMOID CURVE
1.27
MULTIVARIATE LOGISTIC REGRESSION
1.28
DATA CLEANING AND PREPARATION
1.29
BUILDING YOUR FIRST MODEL
1.30
FEATURE ELIMINATION USING RFE
1.31
CONFUSION MATRIX AND ACCURACY
1.32
MANUAL FEATURE ELIMINATION
MACHINE LEARNING ASSOCIATE (PART 2)
19
2.1
METRICS BEYOND ACCURACY: SENSITIVITY & SPECIFICITY
2.2
FINDING THE OPTIMAL THRESHOLD USING ROC CURVE
2.3
METRICS BEYOND ACCURACY: PRECISION& RECALL
2.4
INTRODUCTION TO KNN
2.5
HOW KNN WORKS.
2.6
PROS AND CONS OF KNN
2.7
APPLICATION OF KNN
2.8
KNN MODEL BUILDING
2.9
EVALUATING A KNN MODEL.
2.10
UNSUPERVISED LEARNING: CLUSTERING (INTRODUCTION)
2.11
UNDERSTANDING CLUSTERING MODELS
2.12
PRACTICAL EXAMPLE OF CLUSTERING – CUSTOMER SEGMENTATION
2.13
K MEANS CLUSTERING (INTRODUCTION) .
2.14
PRACTICAL CONSIDERATION IN K MEANS ALGORITHM
2.15
CLUSTER TENDENCY
2.16
K MEANS IN PYTHON CASE: IRIS DATASET CLUSTERING
2.17
HIERARCHICAL CLUSTERING ALGORITHM
2.18
UNSUPERVISED LEARNING: PRINCIPLE COMPONENT ANALYSIS (PCA)
2.19
PCA: IRIS DATASET
ADVANCED MACHINE LEARNING
15
3.1
INTRODUCTION.
3.2
DECISION TREE ON WINE DATASET (Python Implementation)
3.3
CONCEPT OF HOMOGENEITY
3.4
GINI INDEX
3.5
ENTROPY AND INFORMATION GAIN
3.6
SPLITTING BY R-SQUARED
3.7
DECISION TREE HYPERPARAMETER TUNING
3.8
TREE TRUNCATION
3.9
RANDOM FOREST ENSEMBLE BAGGING TECHNIQUE
3.10
BAYES THEOREM AND ALGORITHM BUILDING BLOCKS
3.11
NAIVE BAYES: TEXT CLASSIFICATION HAM VS SPAM CASESTUDY.
3.12
SUPPORT VECTOR MACHINE
3.13
BOOSTING: INTRODUCTION, ADABOOST, GRADIENT BOOSTING, XGBOOST
3.14
ARTIFICIAL NEURAL NETWORKS.
3.15
ADVANCED ML CONCEPTS.
Machine Learning Associate.
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