Machine Learning

Course Description:

Machine Learning focuses on creating algorithms to find patterns or make predictions from experimental data. The growing field of Machine Learning has a vast range of applications in different fields like Intelligent Systems, computer vision, Speech Recognition, Natural Language Processing, Robotics, finance, information retrieval, healthcare, weather prediction. Machine Learning Master's program develops theoretical and practical fundamentals required to be at the front of progress in the next technical revolution. The enhancements completed in Machine Learning and its related disciplines will soon trace every part of technology.

Course Objective:

• After completion of this course, trainee will be able to:
• Master the concept of Python Programming.
• Clearly understand the Machine Learning concept.
• Understand the concept of Deep Learning, Natural Language Processing, Graphical modelling and Reinforcement learning.
• Understand the theory underlying machine learning algorithms.
• Use machine learning to make decisions and predictions.
• Select appropriate statistical and predictive methodologies.

This training is suitable for:

• Engineers.
• Software and IT professionals.
• Data Professionals.
• Data Scientists.
• Machine Learning professionals.

Internationally Accepted Certificate

Duration of Course:

• 60+ hours

Topics Covered are:

• Basic Python Programming constructs
• An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script.

Functions and OOP in Python, NumPy basics:

• Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

Learn Pandas basic concepts, work with Series:

• Learn about the Pandas Data Frame, the superior alternative to the Python list and dictionary built on NumPy, and the de facto standard to work with tabular data in Python.

Work with Pandas Data Frame (Advanced):

• You will learn how to tidy, rearrange, and restructure your data using versatile pandas Data Frames.

Working with Visualizations:

• You will learn to build various types of plots and to customize them to make them more visually appealing and interpretable.

Introduction to Machine Learning:

• Learn to convert a Business problem to a Machine Learning problem. # The problem: I want to predict the price of a house in NY. How to do it?

Descriptive Statistics:

• Summarize the Housing Price dataset

Inferential Statistics:

• Learn the Art of Statistical Inference and draw conclusions from your data. Find if raising the price of a House caused a meaningful drop in sale.

Exploratory Data Analysis:

• Make sure you know what the question is you are trying to answer and form a hypothesis prior to jumping to ML. Visualize the data to gain further insights about the dataset.

Linear Regression:

• Make your first prediction with our favourite ML algorithm!

Advanced Linear Regression:

• Improve the predictive power of your linear regression using Regularization techniques
• Feature Engineering
• Be creative, learn the magic of transforming your feature set for best model outcomes

Feature Selection:

• Improve the quality of your model by selecting the relevant features, rejecting redundant and noisy features

Logistic Regression:

• Take the basic classification challenge - Learn to predict if your wife is angry with you or not - the ML way!

Decision Trees:

• Use this supervised learning algorithm where we first construct the tree with historical data, and then use it to predict an outcome

Challenges in Machine Learning:

• Learn how to handle some of the practical challenges faced in solving a ML problem, and how to deal with them

Clustering/k-means:

• Learn how to cluster, transform, visualize, and extract insights from unlabelled datasets using scikit-learn and scipy.

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