TABLE OF CONTENTS

Overview  

MACHINE LEARNING is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Artificial Intelligence will shape the future of humans more powerfully than any other innovation in the twenty-first century. With data growing rapidly and in varieties, the world is switching over to a more powerful and cheaper computational processing that includes affordable data storage

THE SEMANTIC TREE: Artificial Intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Machine Learning is a sub-field of Artificial Intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.

WHO SHOULD STUDY THIS?

  • Technical people who wish to re-visit Machine Learning quickly.
  • Non-technical people who want an introduction to Machine Learning, but have no idea about where to start.

 

  • Anybody who is interested in this new subject and wants to have a crack at it.

PRE-REQUISITES: To understand the concepts presented the following are recommended:

  • In-depth knowledge of intro-level Algebra – Variables, Coefficients, Linear Equations, Calculus and Understanding of Graphs.

 

  • Proficiency in Programming basics, experience in Python coding. Should feel comfortable reading & writing Python code –that contain basic Programming constructs like function definitions/invocations Lists, dictionaries, loops, and conditional expressions.

 

  • Basic knowledge of the following Python libraries:  1)NumPy 2) Pandas  3)SciKit-Learn 4)  SciPy  5) Matplotlib ( and/or Seaborn)

 

  • Introduction to Computer Science: chapters 1-4

EDUCATIONAL OBJECTIVES: This program will teach you to become a MACHINE LEARNING ENGINEER, build models and apply them to data sets in fields like Finance, Healthcare, Education, etc

                      DURATION OF THE PROGRAM: One term of 3 months –10 hours/week – 100 hours

[ The duration is an estimation of the total hours an average student may take to complete all required course-work, inclusive of lecture time and project work.]

About the Course

 

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we’ll explore some basic machine learning concepts and load data to make predictions.

Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. In this course, we will use machine learning to build a value estimation system that can deduce the value of a home.   Although the tool we will build in this course focuses on real estate, you can use the same approach to solve any kind of value estimation.

 

WHAT YOU’LL LEARN

 

  • Install environment to test Machine learning
  • Understand basic machine learning vocabulary
  • Exposure to Machine Learning Frameworks
  • Understand Supervised Machine Learning
  • Create a basic home estimator calculator
  • Load a Dataset
  • Make Predictions from a dataset

 

Program Benefits

 

1.       Simplifies Product Marketing and Assists in Accurate Sales Forecasts

2.      Facilitates Accurate Medical Predictions and Diagnoses

3.      Simplifies Time-Intensive Documentation in Data Entry

4.      Improves Precision of Financial Rules and Models

5.      Easy Spam Detection

6.      Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry

7.      Better Customer Segmentation and Accurate Lifetime Value Prediction

8.     Recommending the Right Product

Job Outlook

 

There are multiple and different career paths in the area of Machine Learning and also the average salaries are also big figures in the Machine Learning career path. This suggests that the future for one who wants to enter into the area of Machine Learning will be bright and exciting. There will be huge numbers of requirements for the persons with skills set in the area of Machine Learning in the coming future

 

Real Career Impact

 

Machine Learning Professionals are highly required in the area of Information Technology Industry across the world especially in the USA. Machine Learning reduces a lot of human efforts easily by reducing pain and errors. Most of the companies are starting automation and those also need the Machine learning technology to be implemented across their business units to increase their performance and efficiency while reducing the costs.

 

Job Aspiration

 

Machine Learning Engineer      Data Architect     Data Scientist

 

Data Mining Specialists      Cloud Architects     Cyber Security Analysts

 

Salary: $125,000 to $175,000.

Electives:

  • Machine Learning for Text Mining
  •  Algorithms for NLP
  • Machine Learning for Signal Processing
  • Cloud Computing
  • Visual Learning & Recognition

 

WHAT IS MACHINE LEARNING?

In simple terms, Machine Learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code which is specific to a problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data

Machine Learning algorithms fall into three categories:

  • Supervised Learning
  • Unsupervised learning
  • Reinforcement Learning

 

Supervised Learning: This algorithm takes labeled data and creates a model that can make predictions given new data. This can be either a ‘classification ‘ problem or a ‘regression’ problem.

 

Unsupervised Learning: given data that has not been labeled or categorized, the goal is to find patterns and create structure in data in order to derive meaning. The two forms of unsupervised learning are ‘clustering’ and ‘dimensionality reduction’

Reinforced Learning: this method uses a reward system and trial-and-error method to maximize the long-term reward

MACHINE LEARNING PROCESS:

 

ROADMAP TO BEGIN WITH MACHINE LEARNING:

 

  • Start with Algebra. Linear Algebra with particular attention to – vectors, matrix multiplication, determinants, and Eigenvector decomposition – all play heavily as the cogs that make machine learning algorithms work.
  • Next Calculus should be your focus. Understand the meaning of derivatives and see how they can be used for optimization. Learn the sections in Single Variable Calculus thoroughly and a few introductory sections (1 and 2) of Multivariable Calculus.
  • Be thorough with Python libraries used in machine learning. (Refer Pre=requisites).
  • Get coding! It’s advised to implement all algorithms from ‘scratch’ in Python before using the pre-made models in SciKit-Learn, as it gives a better in-depth knowledge of how it works. The following order is suggested:

Linear Regression – Logistic Regression – Naive Bayes Classifier –K-Nearest Neighbors

(KNN) – K-Means –Support Vector Machine (SVM) – Decision Trees –Random Forests and Gradient Boosting.

 

ALGORITHM IMPLEMENTATION ROADMAP:

  • Get data to work – Kaggle and UCI are great resources to look out for data sets.
  • Choosing an algorithm(s). Once you have the data in a good place to work with it you can try different algorithms (Refer below)
  • Also, refer Joel Grus’s Github –“ Data Science from scratch’
  • Visualize the data: Python has various libraries such as Matplotlib and Seaborn that help us plot data.
  • Tune the algorithm. All models we implement have tons of buttons and knobs to play around with – hyper-parameters.
  • Evaluate the model. Python library, SKLearn provides a lot of tools to evaluate your model and check for metrics such as accuracy, fi score, precision, etc.

 

 

TENACITY IS KEY;

 

After getting familiar with a few algorithms and concepts, move into one short-term project that is not complex… to begin with.

Failures are bound to happen. Don’t be afraid to fail. Try to spend time figuring out the math and how and why an error popped up.

The small models are a sandbox for learning. Try and try again without losing heart!

 

3  model evaluation and valuation projects will be taught.

 

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