Online Internship Program on Python for Data Science and Machine Learning.
Mode of Conduct for Online: Live Online using Google meet.
Career Transition Program To Data Science would be beneficial for – Fresher Graduates / Fresher Post Graduates / Final year postgraduates /Working Professional
who want to build their career in the field of Data Science, Machine Learning, Deep Learning. Working professional.
Anaconda , Jupyter Notebook , Pycharm , MySQL etc.
3 Months to 4 Months.
● Understanding Python, Data Science, and Machine Learning concepts with 4+ years of an experienced mentor.
● Internship Certificate.
● Live Kaggle Case Studies in Data Science, Machine learning, and Deep Learning.
● 5 Project on Exploratory Data Analysis
● 10 Projects on Machine Learning
● 5 Projects on Deep Learning
| courses | uses | |
|---|---|---|
| Introduction To Python | 1. Why Python 2. Paradigms 3. Diff b/w Python & Other (C,C++) 4. Python history 5. Python features 6. Python programming form 7. Understanding Python Blocks 8. Python Prompt 9. Python Data Types 10. Typecasting 11. Python I/O |
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| Data Structure in Python | 1. List 2. Tuples 3. Dictionaries 4. Set 5. FrozenSet 6. Bytes 7. Bytearray |
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| Control Statement and keywords in Python | 1. Python If 2. Python If else 3. Python else if 4. Python nested if 5. Python for loop 6. Python while loop 7. Python break 8. Python continue 9. Python pass |
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| Function in Python | 1. Defining a Function 2. Invoking/Calling a Function 3. return Statement 4. Function Arguments 5. The Anonymous Functions 6. Normal Functions and Anonymous Function 7. Anonymous function in python 8. Magic Method in python 9. Generators in python |
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| Strings in Python | 1. Introduction to String 2. String operations and indices 3. Basic String Operations 4. String Functions, Methods 5. Delete a string 6. String Slicing |
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| Object-Oriented Concepts in Python | 1. Problems in Procedure Oriented Approach 2. Features of Object-Oriented Programming System (OOPS) 3. Classes and Objects 4. Inheritance 5. Polymorphism 6. Abstraction 7. Encapsulation |
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| Exception Handling in Python | 1. Python Errors 2. Common RunTime Errors in PYTHON 3. Abnormal termination 4. Chain of importance Of Exception 5. Exception Handling 6. Try … Except 7. Try .. Except .. else 8. Try … finally 9. Python Custom Exceptions |
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| FIle Operation in Python | 1. Opening a file 2. Closing a File 3. Writing to a File: 4. Reading from a File 5. Attributes of File 6. Modes of File |
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| Database Management System | 1. Introduction to DBMS 2. Different Keys in DBMS 3. Data Definition Language 4. Schema and Instances 5. Data manipulation language 6. ER Diagram 7. Joins in DBMS 8. Normalization and dependencies 9. Subqueries |
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| Object-Oriented Concepts in Python | ||
| course | types | |
|---|---|---|
| Exploratory Data Analysis Project | 1. Exploratory data analysis on Emergency (911) Calls Fire, Traffic, EMS for Montgomery County, PA. 2. Exploratory data analysis Iris Dataset 3. Exploratory data analysis on Haberman Dataset 4. Exploratory data analysis on Indian Premier League (IPL) Dataset 5. Exploratory data analysis Personal Case Studies |
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| Introduction to Machine Learning | 1. Need of Machine Learning 2. Types of Machine learning 3. Python libraries for Machine Learning 4. Python library Scikit-Learn |
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| Performance measurement of ML models | 1. Accuracy 2. Confusion matrix, TPR, FPR, FNR, TNR 3. Precision & recall, F1-score. 4. Receiver Operating Characteristic Curve (ROC) curve and AUC. |
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| Machine Learning Algorithms | 1. K-nearest neighbors algorithm (KNN) 2. Naive Bayes 3. Linear Regression 4. Logistic Regression 5. Support Vector Machine (SVM) 6. Random Forest 7. Decision Tree 8. Gradient Boosting Machine 9. XGBoosts 10. Kmeans Clustering 11. Cascading classifiers |
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| Deep Learning Algorithms and Case Studies | 1. Convolutional Neural Network(CNN) 2. Recurrent Neural Networks(RNNs) 3. Long Short Term Memory Networks (LSTMs) 4. You Only Look Once (YOLO) |
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| Natural Language Processing | 1. Introduction to NLP 2. Count vectorization 3. Bag of Words 4. Term Frequency-Inverse Document Frequency (TF-IDF) 5. CountVectorizer 6. Word2Vec |
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| Case Studies / Projects | ● Iris flower Classification using ● Fruits classification ● Titanic survival ● Admission prediction for MS ● House Price prediction ● Stock Price Prediction using Machine Learning ● Fake news classifier ● Sentiment detection from text ● Quora Question pair similarity problem ● Covid Fake Mask Detection using CNN ● Real-time object detection using YOLO ● Animal Image Classification using CNN ● Introduction to the self-driving car ● Recommendation system on Flipkart Product dataset ● Student personal case studies |
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| Add on Session | 1. Resume Building 2. Interview Tips 3. One-To-One Mentorship 4. Mock Interviews 5. Job Referrals 6. ML Model Deployment Flask Web Framework |
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