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Overview

Course Description

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, 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 pervasive today that you probably use it dozens of times a day without knowing it.

This course provides  a detailed overview of various algorithms and techniques, such as regression, classification, time series modeling, supervised and unsupervised learning, Natural Language Processing, etc. You will also use Python programming language to write code for implementing numerous algorithms in this certification training.

Course Content

Module 01 - Introduction to Machine Learning

1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning

Module 02 - Supervised Learning and Linear Regression

2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression

Module 03 - Classification and Logistic Regression

3.1 Introduction to classification
3.2 Linear regression vs logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR

Module 04 - Decision Tree and Random Forest

4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest

Module 05 - Naïve Bayes and Support Vector Machine

5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM

Module 06 - Unsupervised Learning

6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA

Module 07 - Natural Language Processing and Text Mining

7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification

Module 08 - Introduction to Deep Learning

8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders

Module 09 - Time Series Analysis

9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis

Capstone Project

Predicting Housing Price using Polynomial Regression Model *

The objective of the project is to predict the house price depending upon various input features by using Polynomial Regression model.

Domain: Real Estate

*Capstone project are subject to change

Student feedback

11 Reviews

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4.9

out of 5

Course Rating

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Prabhas Kumar


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Kshitij Mishra

Amazing Training

Amazing course! Great breakdown on hard topics and elaborate course structure makes it easy to understand and follow the course. One of the best ML courses out there! Highly recommend


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Esita Pathak

Good Course

Excellent, in depth introduction to machine learning. If you're really interested in machine learning, and want to get a very strong base, this one's for you. Also, you guys are always great and this one is no exception.


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Disha Mehra

Great content

Content of this course is very well structured to understand basic approach for implementing any supervised Machine Learning problems. Would love an advanced course as well.


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Akshat Kapoor

Amazing Course

This was my first attempt to learn Machine learning and I learnt a lot over this course. You guys are great in every aspects of providing online teaching. Thanks!


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Salman Ali

Worthy training

It's worth one's time and money, joining a course on SparkAcademy; my experience was great here, I appreciate efforts you guys put in training.


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Satendra Aggarwal

Good Course

Super clear explanations and good examples.


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Kylian Vats

Best Training

Probably the best machine learning course out there. We learnt not only how to build machine learning models from scratch but even learnt a lot of basic programming as well. A lot of explanations were given for every topic.


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Karan Malhotra

Amazing course

Prior to taking this course I had an applied knowledge of machine learning. After taking this course I understand, at a fundamental level, how the algorithms I have been using, work. These algorithms are now demystified and I now know how to write the algorithms from scratch - my two objectives in taking this course.


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Sarthak Satkar Singh

Great course & training

I really enjoyed this course. I HAVE found though that the concepts here set me up to understand the concepts needed to build up my future in this field. Amazing training provided also.


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    Course Features

    • Python
    • Scikit-Learn
    • Decision Tree and Random Forest
    • Naïve Bayes
    • Support Vector Machine (SVM)
    • Kernel
    • Decision Tree
    • Random Forest
    • k-means clustering
    • Principal Component Analysis (PCA)
    • Natural Language Processing (NLP)
    • Text mining
    • NLTK Corpora
    • Deep Learning