Machine Learning & Artifical Intelligence

Course Name : Machine Learning & Artificial Intelligence

Course Contents

Module 01 - 1. Introduction to Machine Learning

Basics of Machine Learning.

What and why Machine Learning.

Applications of Machine Learning.

Types of Machine Learning.

Main Challenges of Machine Learning.

Module 02 - Scikit Learn and Linear Algebra

Introduction to Scikit Learn.

Features of Scikit-Learn.

CONVENTIONS & IMPLEMENTATION STEPS.

Vectors (2D, 3D).

Dot Product,Hyperplane,Square, Rectangle,Hypercube.

Data types and its measures.

Random Variables, its application with variables.

Probability-Application & Probability distribution with examples.

Sampling Funnel-why and how.

Module 03 - Statistics

WHAT IS STATISTICS.

BASIC TERMINOLOGIES IN STATISTICS .

TYPES OF STATISTICS .

DESCRIPTIVE STATISTICS .

MEASURE OF CENTRAL TENDENCY (Mean, median, mode ) .

Measures of dispersion (Variance, Standard Deviation, Range-its derivation ) .

Measures of Skewness & kurtosis .

INFERENTIAL STATISTICS.

Module 04 - Data pre-processing & Exploratory Data Analysis

Is your data clean? & What is Data Pre processing? .

Data cleaning techniques.

2D Scatter-plot & 3D Scatter-plot & Pair plots.

Univariate, Bivariate and Multivariate .

Histogram & Box-plot.

Variance,Standard Deviation,Median & IQR (InterQuartile Range)

Detecting outliers.

Module 05 - Feature Engineering

Introduction & Need for Feature Engineering in Machine Learning.

Steps in Feature Engineering .

Feature Engineering Techniques.

Module 06 - Performance Metrics & Parameter Tuning

Confusion Matrix & ROC Curve.

Cross Validation in Machine Learning .

K fold Cross Validation & Grid search.

Module 07 - Supervised Learning

Linear Regression - Mathematical Intuition.

Programming of Linear Regression in Python-scikit learn.

Difference between regression and classification .

Various Algorithms in Classification .

Logistic Regression & Naive Bayes.

Module 08 - Unsupervised Learning - Clustering & Association Rule Mining

What is Unsupervised Learning & Types of Unsupervised Learning.

Applications of Unsupervised Learning .

Introduction to Clustering Algorithms .

Types of Clustering Algorithms.

What is K-Means Clustering? .

Implementation of K-Means Clustering.

Improving Models.

What is Association Rule Mining?.

Algorithms in Association Rule Mining.

Implementation of Apriori in Python.

Module 09 - Matplotlib and Advanced Probability Concepts

A crash course in Matplotlib.

Covariance and correlation.

Conditional probability.

Module 10 - Planning and implementing Azure Storage

Azure Storage account overview.

Understand Blob Storage.

Understand File Shares.

Configuring Azure FileSync.

Data migration using Azure storage explorer.

Manage Azure Storage permissions.

Azure Static Website deployment.

Module 11 - Apache Spark: Machine Learning on Big Data

Installing Spark & Spark introduction.

Spark and Resilient Distributed Datasets (RDD).

Introducing MLlib & Decision Trees in Spark with MLlib

K-Means Clustering in Spark.

TF-IDF.

Using the Spark 2.0 DataFrame API for MLlib.

Module 12 - Testing and Experimental Design

A/B testing concepts.

T-test and p-value.

Measuring t-statistics and p-values using Python .

Determining how long to run an experiment for.

A/B test gotchas.

Module 13 - Introduction to CNN

Introduction to CNN, Relu layer, pooling.

Flatening, Full connections.

Building CNN models, accuracy of Models.

Image classification using CNN.

Module 14 - Introduction to RNN

Introduction to RNN, Vanishing Gradient Problem, LSTM.

Building RNN models, accuracy of Models.

Forecasting using RNN.