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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
  • Numpy, scify, pandas, scikitlearn etc
  • 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
  • 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
  • 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)
  • Basic Statistics - Measures of Central Tendencies and Variance
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Important modules for statistical methods: Numpy, Scipy, Pandas
  • 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)
  • Linear Regression
  • Segmentation - Cluster Analysis (K-Means) Decision Trees
  • Support Vector Machines(SVM)
  • Other Techniques (KNN, Naïve Bayes, )
  • Important python modules for Machine Learning (SciKit Learn, scipy, etc)
  • Artificial Neural Networks(ANN)

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