Course Content

MACHINE LEARNING WITH PYTHON

W H O W E A R E

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W H A T  W E D O

Training starts with the fundamentals of TensorFlow library which includes variables, matrices, and various data sources .

GETTING STARTED

STATISTICS

Why Statistics?

Introduction to Statistics

Various Data Types

Various Measurement Scales

Exploratory Data Analysis

Measures of Central Tendency

Measures of Dispersion

Practical Approach on EDA

Features of Data Distribution

Data Frames

Comparing Populations Sample Spaces

Confidence Interval

Properties of Probability

Conditional Probability

Independent Events

Random Variables

Discrete and Continuous

Binomial Distribution

Normal Distribution

Probability Distribution

Simple Random Sample

Sampling

Central Limit Theorem

Sampling Distribution

Simulated Sampling

Point Estimation

Confidence Interval for Means

Confidence Interval for Variance

Sample Size and Margin

Errors

Hypothesis

Tests for Proportion

Null Hypothesis

Alternate Hypothesis

Alpha Risk and Beta Risk

Significance of p-value Hands-On on Hypothesis

MACHINE LEARNING WITH PYTHON

Intro to Machine Learning

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Application of Machine Learning

Evolution of Machine Learning

Types of Predictions

Response

Classification

Neuron Based Classification

Simple Linear Regression

Case Study on Linear Regression

Project on Linear Regression

R-Squared Value

Improving the Accuracy

Multiple Linear Regression

Simple Logistic Regression

Confusion Matrix

Multiple Logistic Regression

Various Families in Logistic

Decision Trees Theory

Cancer Prediction with Decision

Improving the Accuracy

Cross-Validation of Tree Project on Decision Tree

Random Forest

Project on Random Forest

K-Means Theory

Various Clusters

Practical of K-Means

Project on K-Means

Intro to K-NN

Case Study on K-NN

Project on K-NN

Concept of Support Vectors

Hyperplanes in SVM Implementation of Kernels

Case-Study on SVM

Project on SVM

Introduction to Deep Learning

Concept of Artificial Neurons

Building Feed Forward NeuralNet

Impact of Hidden Layers

Gradient Descent

Weights Update

MACHINE LEARNING ALGORITHMS

LINEAR REGRESSION

LOGISTIC REGRESSION

DECISION TREE

RANDOM FOREST

NEURAL NETWORK

SUPPORT VECTOR MACHINE

NAIVE BAYES

TEXT MINING

KNN

K-MEANS

REGRESSION TREES

SUPPORT VECTOR REGRESSION

N L P

Intro to Text Mining and NLP

Concept of Document

Corpus

Bag-of-Words

Stemming of Words

Creating Document Term Matrix

Analysing Unstructured Data

10,000 Reviews from Flipkart

Cleaning the Data

Plotting WordCloud

Implementing Naive Bayes

Bayesin Rule

To classify Reviews Improving the Accuracy

MACHINE LEARNING WITH SCIKIT LEARN

Gentle Introduction

Installing scikit-learn

Our first machine learning method – linear classification

Evaluating our results

Machine learning categories Important concepts related to machine learning

ADAVANCED FEATURES

Feature extraction

Feature selection

Model selection

Grid search

Parallel grid search

NONLINEAR CLASSIFICATION AND REGRESSION WITH DECISION TREES

Decision trees

Training decision trees Decision trees with scikit-learn

POST TRAINING

PROJECTS

EXAMINATION

RESUMING

PREPARATION

INTERVIEW

GUIDANCE