Machine learning with Python

10000 Learners


Course Objective:

Objective: Professionals who are want to learn ML with Python training but have no prior knowledge of Python, need to begin with of the following course.

Prerequisite: No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.


Course Content:

TOPICS TO BE COVERED:

Introduction to Machine Learning
  • What is Machine learning
  • The three different types of machine learning
  • Supervised, unsupervised, reinforcement learning
  • An introduction to the basic terminology and notations
  • A roadmap for building machine learning systems
  • Uses of Machine learning in real life example
Introduction to Python
  • Introduction to Python language
  • Environmental setup for Python, Anaconda, Jupyter, Spider etc
  • Data types of Python, numbers, string
  • Run a python program in jupyter/spider/ipython
  • If, elif, Loops in python
  • Functions in python
  • Positional argument, keyword argument, default argument
  • Lambda function
  • Module of Python
  • Build in and user defined module
  • Import a module, call a function of a module.
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • 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
  • How to create class and modules and how to call them?
Object oriented Python
  • Introduction to Object oriented language
  • Class, object
  • Create class and object in python
  • Data hiding, method overloading
  • Inheritance, method overriding
String manipulation and collections in python
  • Creating and accessing strings
  • Basic functions on strings
  • Indexing and slicing on string
  • Strings methods
  • List and its methods
  • Accessing lists
  • Tuple, set, dictionary and their methods
  • Indexing, slicing on list, tuple, set , dictionary
  • List comprehension and its uses
Introduction to Statistics
  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas
Introduction Numeric and Data Analysis Modules of Python
  • Introduction to Numpy
  • Creating 1D and 2D Numpy array
  • Slicing and indexing of Numpy Array
  • Operations on Numpy array
  • Introduction to Pandas
  • Series and DataFrame
  • Operations on series and dataframe
  • Different functions on dataframe
  • Pandas plotting functions
  • Read external dataset using Pandas
Handling Data – Data Wrangling
  • What is Data Wrangling
  • Prepare data for use
  • Introduction to sklearn module of python
  • Find for missing value and impute
  • Fillna() and Imputer() of sklearn
  • Concepts of categorical variable and its problem
  • Solution for categorical problem using dummy variable, LableEncoding
  • Feature Scaling and its solution using StandardScalar
Visualization library of Python
  • Using pandas plotting functions
  • Introduction to Matplotlib and Seaborn
  • Difference between the two
  • Scatter plot using matplotlib
  • Draw histogram, barchart, pie chart to any data
  • Create pairplot using seaborn
  • Concept of Box plot in seaborn
  • Drawing of 3D graphs
Supervised Learning-NAIVE BAYES
  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications.
Supervised Machine learning – Regression & Classification
  • Explain supervised
  • Difference between classification and regression
  • Single and Multiple Linear Regression
  • Mathematics behind linear regression
  • Simple linear regression with example
  • Multiple linear regression with Boston dataset
  • R2 score and RMSE score
  • Logistic regression to solve classification problem
  • Apply logistic regression on titanic dataset
  • Classification report and confusion matrix

Projects:

  • House price prediction
  • Big mart sales prediction
  • Stock market analysis
  • Loan prediction
Supervised Machine Learning – Regression revisited
  • Problem for non linear regression
  • Discussion on tree based algorithm
  • Decision tree regressor with example
  • Mathematics behind decision tree
  • Pruning in decision tree
  • Concepts of ensemble algorithm
  • Random forest regressor with example
  • Gradient descent algorithm
  • Mathematics behind Gradient descent
  • Understand the various elements of machine learning
    algorithm like parameters, hyper parameters, loss
    function, cost function and optimization.
Supervised Machine learning – Classification II
  • Classification algorithm and its uses
  • Different types of classifiers
  • K Nearest Neighbour classifier (KNN)
  • Mathematics behind KNN
  • Apply KNN on IRIS dataset
  • Accuracy test of classification
  • Decision tree classifier for classification problem
  • Random forest classifier with example
Supervised Machine learning – Advanced Classification
  • What is naïve bayes classifier
  • How naïve bayes work?
  • Mathematics behind naïve bayes
  • Text based classification using Naïve Bayes algorithm
  • Classify email using Naïve Bayes classifier
  • CountVectorizer and TFID algorithm to solve text classification problem
Dimensionality Reduction using PCA
  • How to recognize pattern in machine learning
  • Use of MNIST dataset to recognize some hand written digits
  • Feature extraction and Feature elimination
  • PCA to use Dimensionality reduction
  • Problem of time complexity and visualization
  • Solution using PCA
  • Use PCA to recognize MNIST dataset
Model Selection
  • Model selection and its uses
  • Cross validation
  • K- fold cross validation
Unsupervised learning - Clustering
  • What is unsupervised learning
  • Types of unsupervised learning
  • Concepts of clustering
  • K means clustering to cluster group of datas

Projects:

  • Handwritten digit recognition
  • Email spam filtering
  • Customer churn classification
  • Black Friday sale analysis
  • Heart disease prediction
SVM and Kernel trick
  • What is support vector machine
  • Linear SVM
  • Solving non linear problem using SVM
  • Kernel and kernel trick to solve non linear problem
  • Polynomial and RBF kernel
  • Gamma and C parameter in SVM
  • Effect of gamma and C
Association Rules and Recommendation Engine
  • What is association rule and its parameter
  • Measure of effectiveness of the rule
  • Market basket analysis for association
  • Recommendation engine
  • Content based filtering
  • Collaborating filtering
  • User-user and item –item collaboration
  • Example using Movie Lens dataset
Time Series Analysis
  • What is Time Series Analysis (TSA)
  • Components of TSA
  • ARMA and ARIMA model
  • Movie Average(MA) model
  • Choosing the model
  • Model implementation in python
  • Analysis of GDP growth of UK
Boosting
  • Boosting algorithm and how it works
  • Types of boosting algorithm
  • AdaBoost and gradient boosting algorithm
  • Mathematics behind boosting algorithm/li>
  • Boosting algorithm with example
Ensemble Learning
  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost.
Introduction to Deep Learning
  • Concepts of neural network and deep learning
  • Introduction to tensor flow
  • Tensor flow constant, variable and place holder
  • Artificial neural network and tensor flow
  • ANN, RNN, and CNN

Projects:

  • Movie recommendation system
  • Face detection
  • Object recognition
  • Text classification
  • Sentimental analysis

Key Features

  • Gain skills and competencies required in Industry by Experts.
  • Work on Real-time Projects depending upon the course you select.
  • Students work in a professional corporate environment.
  • Get a globally recognized Certificate form WebTek with our partner logos.
  • Global Brand recognition for Placements.
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Includes

  • Course Duration: 6 Months
  • Regular Batches: Online / Offline/ Weekend
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