Data Science for Beginners: Hands-On Data Science in Python
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- All Levels
- Duration
- 12 hours
- Lessons
- 116 lessons
- Certificate
- Available on completion
- Course author
- Vijay Gadhave
-
- The Complete Understanding of Machine Learning from the Scratch
- Learn Python for Data Science and Machine Learning
- Artificial Neural Networks (ANNs) with TensorFlow 2.0
- Convolutional Neural Networks (CNNs) with TensorFlow 2.0
- Deep Learning and Neural Networks
- Learn How to Pre-Process the Data
- Perform Linear and Logistic Regressions in Python
- Learn Different Regression Algorithms in Python
- Learn to Apply Different Classification Algorithms in Python
- K means and Hierarchical Cluster Analysis
- Data Analysis with NumPy and Pandas
- Data Visualization with Matplotlib library
- DataFrames, Pandas Series, Pandas Matrix
- NumPy Arrays, Indexing, Selection, Numpy Operations
- Learn to Work with Missing Data
- Natural Language Processing
- Dimensionality Reduction: PCA and LDA
Description
Data Science, Machine Learning and Artificial Intelligence are the most demanding skills in today's world,
Almost every Multi-National company is working on these new technologies
With this Mega Course you will learn all the required tools for Data Science from the very beginning !
We will cover below topics,
1) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling
2) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Decision Tree, Random Forest, Evaluating the Model Performance
3) Classification Techniques: Logistic Regression, KNN, SVM, Naïve Bayes, Decision Tree, Random Forest
4) Cluster Analysis: K means, Hierarchical
5) Natural Language Processing: NLTK, Tokenization, Stemming, Lemmatization, Stop Words, POS Tagging, Chunking, Named Entity Recognition, Text Classification
6) Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
7) Artificial Neural Networks (ANNs) with TensorFlow 2.0
8) Convolutional Neural Networks (CNNs) with TensorFlow 2.0
9) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations
10) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files
11) Data Visualization with Matplotlib library
Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way !
Regards,
Vijay Gadhave
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