Machine Learning Training in Noida

● Syntax and Semantics of Python programming
● Python History
● Versions of Python
● Features of Python
● Input and Output Functions
● Variable and Data Types
● What is a Variable
● Typecasting
● Data Types in Python
o Numeric
o String
o Boolean
o Compound
o List
o Tuple
o Set
o Frozen Set
o Dictionary
● Operators in Python
● Conditional Statements in Python
● Loops in Python
● User defined functions in python
● Strings in Python

● Creating arrays
● Array indexing
● Array slicing
● Numpy Data Types
● Copy vs View
● Array Shape
● Array reshape
● Iterating
● Join
● Search
● Filter
● Split
● Sort

● Pandas Series
● Pandas DataFrame
● Read CSV
● Read JSON
● Cleaning Data
● Missing Value Handling
● Optimizing Data Format
● Redundancy Minimization
● The corr() function
● Plotting Graphs in pandas

● High-Level overview of Data Science / Machine Learning project management methodology
● Random Variable and its definition
● Probability and Probability Distribution
● Balanced vs Imbalanced datasets
● Sampling Funnel, its application and its components
● Measure of central tendency
● Measure of Dispersion
● Expected value of probability distribution
● Measure of Skewness
● Measure of Kurtosis
● Various graphical techniques to understand data
● Installation of Python IDE
● Anaconda and Spyder
● Working with Python with some basic commands
● Normal Distribution
● Standard Normal Distribution / Z distribution
● Z scores and Z table
● QQ Plot / Quantile-Quantile plot
● Sampling Variation
● Central Limit Theorem
● Sample size calculator
● T-distribution / Student’s-t distribution
● Confidence interval

● Parametric vs Non-parametric tests
● Formulating a Hypothesis
● Choosing Null and Alternative hypothesis
● Type I and Type II errors
● Comparative study of sample proportions
● ANOVA
● 2 Proportion test
● Chi-Square test
● Non-Parametric test
● Non-Parametric test continued
● Hypothesis testing using Python

● Scatter Diagram
● Correlation Analysis
● Principles of Regression
● Introduction to Simple Linear Regression
● R shiny and Python Flask
● Multiple Linear Regression
● Scatter diagram
● Ordinary least squares
● Principles of regression
● Splitting the data into training, validation and testing datasets
● Understanding Overfitting vs Underfitting
● Generalization error and Regularization techniques
● Introduction to Simple Linear Regression
● Heteroscedasticity / Equal Variance
● LINE assumption
● Multiple Linear Regression
● Model Quality metrics
● Deletion diagnostics

● Principles of Logistic Regression
● Types of Logistic Regression
● Assumption and Steps in Logistic Regression
● Analysis of Simple Logistic Regression result
● Multiple Logistic Regression
● Confusion matrix
● Receiver operating characteristics curve (ROC curve)
● Lift charts and Gain charts
● Lasso and Ridge Regressions
● Logit and Log Likelihood
● Category Baselining
● Modeling Nominal categorical data

● Supervised vs Unsupervised learning
● Data Mining Process
● Measure of distance
● Types of Linkages
● Hierarchical Clustering / Agglomerative Clustering
● Non-clustering
● Why dimension reduction
● Advantages of PCA
● Calculation of PCA weights

● Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
● Greedy algorithm
● Measure of Entropy
● Attribute selection using Information Gain
● Ensemble techniques
● Decision Tree C5.0 and understanding various arguments
● Random Forest and understanding various arguments
● Boosting / Bootstrap Aggregating
● AdaBoost / Adaptive Boosting
● Stacking
● Gradient Boosting
● Extreme Gradient Boosting (XGB)
● Artificial Neural Network
● Biological Neuron vs Artificial Neuron
● ANN structure
● Activation function
● Network Topology
● Support Vector Machines
● Classification Hyperplanes
● Best fit “boundary”
● Kernel Trick

● Iris Flowers Classification
● Cartoonify Image
● Loan Default Prediction
● Real Estate Price Prediction
● Stock Price Prediction
● Titanic Survival Classification