Best Machine Learning Training in Chennai-Vikapri Training

Description
Curriculum

Course Details

Machine learning is part of AI that enables better performance of the system. Its algorithms aim to detect every issue on computer applications.

This course allows you to use Data Science in the real world. One can learn Python with Data Science to Master this course. Hence, proficient in data analysis automation using Python.

Get in-depth knowledge in techniques of evaluation, experimentation, and project deployment. Also, learn supervised, unsupervised, reinforcement modelling and machine learning concepts.

We offer the best Machine Learning with Python training in Chennai with Certification & Placement support. One-on-one training session by Industry Experts.

Lessons 1: Introduction to Python

Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas,
Installing & loading Packages & Name Spaces

Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values Basic Operations - Mathematical - string - date Reading and writing data Simple plotting
Control flow & conditional statements Debugging & Code profiling

Lessons 2: Scientific distributions used in python for Data Science

Numpy, scify, pandas, scikitlearn etc

Lessons 3: Accessing/Importing and Exporting Data using python modules

Importing Data from various sources (Csv, txt, excel, access etc) Database Input (Connecting to database)
Viewing Data objects - subsetting, methods

Exporting Data to various formats
Important python modules: Pandas

Lessons 4: Data Manipulation – cleansing – Munging using Python modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived
variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Formatting data

Lessons 5: Data Analysis – Visualization using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Lessons 6: Basic statistics & implementation of stats methods in Python

Basic Statistics - Measures of Central Tendencies and Variance
Inferential Statistics -Sampling - Concept of Hypothesis Testing

Important modules for statistical methods: Numpy, Scipy, Pandas

Lessons 7: Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling
Types of Business problems - Mapping of Techniques - Regression vs. classification vs.
segmentation vs. Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)