Machine Learning with Python
About Machine Learning
Machine Learning is simply making a computer perform a task without explicitly programming it. In today’s world, every system that does well has a machine learning algorithm at its heart. Take for example Google Search engine, Amazon Product recommendations, LinkedIn, Facebook etc, all these systems have machine learning algorithms embedded in their systems in one form or the other. They are efficiently utilising data collected from various channels which helps them get a bigger picture of what they are doing and what they should do.
Python is considered as a very efficient Machine Learning tool, it’s very popular too. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community.
This advanced level Machine Lerning course focuses on the following key areas:
- Programming in Python
- Business Statistics for Machine Learning
- Machine Learning Algorithms
- Deep Learning
What We Offer
Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Class Room Training With Flexible Timing
Unlimited Access to Exclusive Study Materials On Cloud
Chapter-1: Python for Machine Learning
1.1 Programming Basicsand environment, Matplotlib
1.2 Python Data Types
1.3 Structures and conditional statements
1.4 Python core packages
1.5 Introduction to Jupyter Notebook
1.6 Introduction to Numpy and Pandas
1.7 Data filtering and selecting
1.8 Find duplicates and treating missing values
1.9 Concatenate and transform data
Chapter-2: Business Statistics for Machine Learning
2.1 Business Statistics and Exploratory Analysis
2.2 Descriptive summary statistics with Numpy
2.3 Summarize continuous and categorical data
2.4 Outlier analysis
3.1 K-Means clustering
3.2 Hierarchical Clustering
Chapter-4: Machine Learning Algorithms (Supervised)
4.1 Linear Regression
4.2 Multiple Linear Regression – Data set and Business Problem, Theory, Backward Elimination.
4.3 Logistic Regression (Case Study)
4.4 Support Vector Regression (Case Study)
4.5 Decision Tree (Case Study)
4.6 Random Forest (Case Study)
Chapter-5: Deep Learning
5.1 Neural Network
5.2 Convolution Neural Network (Keras)
Chapter-6: Machine Learning Algorithms (Unsupervised)
6.1 K-Nearest Neighbors (KNN) concept and application (Case Study)
6.2 Naive Bayes concept and application
Project on 50 Company Data. Project on building ChatBot with IBM Watson API.