 # Machine Learning Track

Machine Learning is one of the most exciting and a very high demand field in the entire industrial world. This course will act as a comprehensive introduction to various topics in machine learning. At the end of the course the candidates will be capable of designing and implementing machine learning solutions to classification, regression, and clustering problems.It also enables them to evaluate and interpret the results of the algorithms. Includes:

• 7 hours of lecture Videos
• 164 hands-on practice exercises
• 63 Assessment exercises
• 290 knowledge based questions

### Stat and Math Fundamentals

Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. This course provides an introduction to the mathematical foundation for the Machine Learning concepts.

COURSE OBJECTIVES

Upon successful completion of the course, the learner will be able to
• Relate the usage of the mathematical concepts in Machine Learning.
• Understand what linear algebra is and why it is relevant and important to machine learning.
• Know what a vector and matrix are, and how to perform arithmetic operations on them.
• Transform observations into information using Statistics.
• Find optimal solutions for equations using Calculus.
• Understand Probability and learn the base for developing Probabilistic Models.

## Course Content

### Introduction to statistics

In this module you will learn how to collect, organize, analyse, and interpret data for the design of surveys and experiments.

• 2 Videos
• 3 Hours
• 31 Problems

### Descriptive statistics

In this module you will learn to provide a brief summary of the samples and the measures done on a particular study.

• 2 Video
• 4 Hours
• 36 Problems

### Probability for ML

In this module you will learn how to predict the likelihood of future events using probability, which is the base for developing Probablistic Models.

• 1 Video
• 3 Hours
• 31 Problems

### Inferential Methods

In this module you will learn how to calculate statistical data for a huge population using Inferential Methods

• 1 Video
• 3 Hours
• 22 Problems

### Algorithm & Models

In this module you will learn how to intepret a problem as a mathematical or algorithmic model.

• 1 Video
• 3 Hours
• 12 Problems

### Linear Algebra for ML

In this module you will learn how to perform operations on Vectors and Matrices, which would be the storage structures for the features of the data.

• 1 Video
• 5 Hours
• 24 Problems

### Calculus for ML

In this module you will learn how to integration and differentiation in equation, which would be helpful for finding optimal solutions.

• 1 Video
• 5 Hours
• 24 Problems

### Exploratory Analytics for Machine Learning

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. This course provides an insight on various ways of representing data and analysing it.

COURSE OBJECTIVES

Upon successful completion of the course, the learner will be able to
• Summarize a given data set using Descriptive Statistics.
• Represent given data as dataframes and work on it logically using control constructs and functions.
• Graphically analyse the data by ploting various graphs.
• Do Statistical Inference using Hypothesis Testing.
• Perform Data-Cleaning and Preprocessing, which is the most important step in Machine Learning, as the end result depends on this solely.

## Course Content

### Descriptive Statistics

In this module you will learn how to summarize a given data set using Descriptive Statistics.

• 1 Video
• 3 Hours
• 19 Problems

### Analyzing data using functions, loops and data frames

In this module you will learn how to represent given data as dataframes and work on it logically using control constructs and functions.

• 1 Video
• 4 Hours
• 23 Problems

### Graphical analysis

In this module you will learn how to graphically analyse the data by ploting various graphs.

• 1 Video
• 3 Hours
• 15 Problems

### Hypothesis testing

In this module you will learn how to do Statistical Inference using Hypothesis Testing.

• 1 Video
• 3 Hours
• 15 Problems

### Data cleaning and pre-processing

In this module you will learn how to perform Data-Cleaning and Preprocessing, which is the most important step in Machine Learning, as the end result depends on this solely.

• 1 Video
• 4 Hours
• 19 Problems

### ML in Python

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. In the past few years, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so unavoidably common today that we probably use it dozens of times a day without knowing it. The best way to make progress towards human-level AI is through Machine Learning. This course teaches you how to implement the various Machine Learning algorithms and its applications.

COURSE OBJECTIVES

Upon successful completion of the course, the learner will be able to
• Develop a good understanding of the fundamental concepts of Machine Learning
• Gain skills in working with algorithms that underpin popular machine learning techniques
• Build expertise on the underlying mathematical relationships within and across Machine Learning algorithms
• Explore the paradigms of supervised and unsupervised learning
• Acquire hands-on experience of working with various machine learning algorithms in a range of real-world applications

## Course Content

### Linear Regression

In this module you will learn how to perform the task to predict a dependent variable value based on a given independent variable using Linear regression.

• 1 Video
• 6 Hours
• 48 Problems

### Logistic Regression

In this module you will learn how to perform the task to predict a dependent variable value based on a given independent variable using Logistic regression and how the Logistic regression becomes a classification technique.

• 1 Video
• 6 Hours
• 48 Problems

### Decision Trees

In this module you will learn how to create a tree-like model of decisions using Decision Tree algorithm.

• 1 Video
• 5 Hours
• 33 Problems

### Model Selection and Cross Validation

In this module you will learn how to compare different machine learning algorithms, and choose the best one.

• 1 Video
• 4 Hours
• 28 Problems

### Neural Networks

In this module you will learn how to create a Neural Network Model and use it for predicting the class.

• 1 Video
• 4 Hours
• 28 Problems

### SVM

In this module you will know how a learned SVM model representation can be used to make predictions for new data.

• 1 Video
• 4 Hours
• 28 Problems

### Random Forest and Boosting

In this module you will learn how to build a classification algorithm consisting of many decisions trees.

• 1 Video
• 5 Hours
• 33 Problems

## Get in Touch with Us

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