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Machine Learning (Python)

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    Course Overview

    Machine learning with python is a comprehensive training course that helps you take a deep dive into machine learning basics and understand how Python programming language integrates to the core. The machine learning with python course is focused on delivering best-in-class learning on statistical modelling, regression and clustering algorithms and have an in-depth understanding of the interoperability between python and machine learning and the magical applications it can create.

    Technofine24 is a prominent leader in offering you an opportunity to transform your theoretical knowledge on machine learning and python, into the much preferred and job-oriented practical skill through multiple projects included in machine learning using python training and certification course. Our certified and seasoned instructors work to build a solid foundation on developing algorithms, creating functions, exception handling, data analysis etc.

    Our diverse training delivery modes of machine learning using python course, offered in custom schedules, help corporate and individual learners find a best-fit learning solution mapping their career and training goals.

    COURSE CONTENT

    1. Introduction to Data Science

    1. What is Data Science?
    2. What does Data Science involve?
    3. Era of Data Science
    4. Business Intelligence vs Data Science
    5. Life cycle of Data Science
    6. Tools of Data Science
    7. Application of Data Science

    3. Feature Engineering

    1. What is Feature?
    2. Feature Engineering
    3. Feature Engineering Process
    4. Benefit
    5. Feature Engineering Techniques

    2. Exploratory Data Analysis

    1. Introduction
    2. Stages of Analytics
    3. CRISP DM Data Life Cycle
    4. Data Types
    5. Introduction to EDA
    6. First Business Moment Decision
    7. Second Business Moment Decision
    8. Third Business Moment Decision
    9. Fourth Business Moment Decision
    10. Correlation

    4. Inferential Statistics and Hypothesis Testing

    1. What is classification?
    2. Types of classification
    3. Naive Bayes Algorithm
    4. Decision Tree Algorithm
    5. Random Forest Algorithm
    6. Support Vector Machine Algorithm

    MACHINE LEARNING

    5. Linear Regression

    1. Simple Linear Regression
    2. Simple Linear Regression In Python
    3. Multiple Linear Regression
    4. Multiple Linear Regression In Python
    5. Industry Relevance Of Linear Regression

    7. KNN Classifier

    1. Data mining classifier technique
    2. Application of KNN classifier
    3. Lazy learner classifier
    4. Altering hyperparameter(k) for better accuracy

    9. Decision Tree Classifier

    1. Rule based classification method
    2. Different nodes for develop decision trees
    3. Discretization
    4. Entropy
    5. Greedy approach
    6. Information gain

    11. Time Series Analysis

    1. Difference between cross sectional and time series data
    2. Different component of time series data
    3. Visualization techniques for time series data
    4. Model based approach
    5. Data driven based approach

    13. Dimensionality Reduction

    1. Dimension reduction
    2. Application of PCA
    3. PCA & its working
    4. SVD & its working

    6. Logistic Regression

    1. Univariate Logistic Regression
    2. Multivariate Logistic Regression: Model
    3. Building And Evaluation
    4. Logistic Regression:
    5. Industry Applications

    8.SVM Classifier

    1. Black box
    2. SVM hyperplane
    3. Max margin hyperplane
    4. Kernel tricks for non linear spaces

    10.Ensemble Learning

    1. Challenges with standalone model
    2. Reliability and performance of a standalone model
    3. Homogeneous & Heterogeneous Ensemble Technique
    4. Bagging & Boosting
    5. Random forest
    6. Stacking
    7. Voting & Averaging technique

    12. Clustering

    1. Difference between Supervised and Unsupervised Learning
    2. Prelims of clustering
    3. Measuring distance between record and groups
    4. Linkage functions
    5. Dendrogram

    13. Projects

    Projects on Machine Learning focused on implementing the concepts of Machine Learning Algorithms in real world problems.

    Giving Computers the Ability to Learn from Data

    1
    The Course Overview
    2
    Transforming Data into Knowledge
    3
    Types of Machine Learning

    Training Machine Learning Algorithms for Classification

    1
    Implementing a Perceptron Algorithm in Python
    2
    The Iris Dataset
    3
    Training the Perceptron
    4
    Improving the Visualization
    5
    Adaline in Python
    6
    Feature Standardization
    7
    Implementing Adaline

    A Tour of Machine Learning Classifiers Using Scikit-Learn

    1
    Scikit-Learn Perceptron
    2
    Logistic Regression in Scikit-Learn
    3
    Predicting Class Probabilities
    4
    Training a Support Vector Machine in Scikit-Learn
    5
    The Effect of Gamma
    6
    Decision Trees

    Building Good Training Sets – Data Preprocessing

    1
    Handling Data
    2
    Mapping Ordinal Features
    3
    Feature Scaling
    4
    Feature Importance’s with Random Forests
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    4.0
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