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Course Overview
Our course introduces learners to Python for analyzing and modeling data. This course provides essential skills for professionals in data analysis, business intelligence, and artificial intelligence. In addition to Python Programming basics, the course also covers machine learning algorithms like regression, classification, clustering, and neural networks. You will also gain practical experience through hands-on projects involving building predictive models and analyzing large datasets. By the end of the course, you will be able to build machine learning models using Python libraries such as Scikit-learn, Pandas, and NumPy. Additionally, you will be able to apply newly acquired skills to real-world business problems enabling you to make data-driven decisions and unlock valuable insights from large datasets.
Admission Is Going On
Enroll now to any of our Offline (On-Campus) or Online (Live Class) courses as per your suitable time.
Course Curriculum
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- Tools: PyCharm, JupyteLab, SQL Server, Git & GitHub
- Class #01 Environment Setup, Variable, Data Type & Type Casting
- 1. Python environment setup.
- 2. Basic syntax of python (statements, indentation, comments).
- 3. Python variable.
- 4. Data type in python.
- 5. Type casting in python.
- Assignment- #1:
- Class #02 String of Python
- 1. Introduction & declaration of string.
- 2. Accessing values & updating string.
- 3. String formatters & escape sequences.
- 4. String functions and operations.
- 5. Most important built-in methods of string.
- Assignment- #2:
- Class #03 Operators & Condition
- 1. Operators & Operands.
- 2. Arithmetic, comparison & logical operators.
- 3. Assignment, Boolean & membership operators.
- 4. If, if.... else, & if...elif...else statement.
- 5. Nested if & nested if. Else statement.
- Assignment- #3:
- Class #04 Loops in Python
- 1. For loop statement.
- 2. While loop statement.
- 3. Infinite & nested loop statement.
- 4. Break, continue & pass statement.
- Assignment- #4:
- Class #05 Built-in Data Structure in Python
- 1. List in python.
- 2. Indexing, slicing & negative indexing in python.
- 3. Tuple & sets in python.
- 4. Dictionary in python.
- Assignment- #5:
- Class #06 Functions in Python
- 1. Define a function.
- 2. Function arguments.
- 3. Recursion in python.
- 4. Built-in python.
- 5. Anonymous function or lambda expression.
- Assignment- #6:
- Class #07 File & Exception Handling
- 1. Open, read & write a file.
- 2. Managing director, & rename a file.
- 3. Errors vs exception.
- 4. Try...except & try...except...else statement.
- 5. Try...except...finally statement.
- Assignment- #7:
- Class #08 Module & Package
- 1. Module vs package.
- 2. Create & uses a module.
- 3. Built-in modules (datetime module).
- 4. Create & uses package.
- 5. pip & PyPI.
- Assignment- #8:
- Class #09 Class, Objects & Inheritance (OOP)
- 1. Class & objects.
- 2. Methods vs functions & magic (under) methods.
- 3. Inheritance in python.
- 4. Polymorphism in python.
- 5. Constructors & Destructors in python.
- Assignment- #9:
- Class #10 Git & GitHub
- 1. Git vs GitHub.
- 2. Install & configure git.
- 3. Create GitHub account.
- 4. Create repository & deploy on GitHub.
- 5. Fundamental bash commands.
- Assignment- #10:
- Class #11 SQL-1
- 1. The Relational Database Management System (RDBMS): An Overview.
- 2. Normalization of Databases.
- 3. Databases Without SQL.
- 4. Statement of Selection.
- 5. Using the WHERE Clause to Filter and Join Multiple Conditions.
- 6. Sort by: distinct, top, like, etc.
- 7. Modifying Syntax.
- Assignment- #11:
- Class #12 SQL-2
- 1. SQL server join types.
- 2. Data Analysis using NumPy.
- Assignment- #12:
- Class #13 Data Analysis using NumPy
- 1. A concise introduction.
- 2. Installation instructions.
- 3. NumPy arrays.
- 4. Built-in methods.
- 5. Array methods and attributes.
- 6. Indexing and slicing.
- 7. Broadcasting.
- 8. Layout.
- 9. Boolean masking.
- 10. Arithmetic operations.
- 11. Universal functions.
- 12. Overview of exercises.
- 13. Solutions to exercises.
- Assignment- #13:
- Class #14 Pandas (part-1)
- 1. An overview in brief and guidelines for installation.
- 2. Introduction to Pandas.
- 3. Data Structures for Pandas – Series.
- 4. Data Frame: Pandas Data Structures.
- Assignment- #14:
- Class #15 Pandas (part-2)
- 1. Hierarchical Indexing.
- 2. Handling Missing Data.
- 3. Data Wrangling.
- 4. Useful Methods and Operations.
- Assignment- #15:
- Class #16 Data Analysis Project Using NumPy and Pandas
- 1. Project One (Which we will download a csv file from kaggle website).
- Assignment- #16:
- Class #17 Statistics Part-1
- 1. Quantitative Analysis.
- 2. Frequency Distribution.
- 3. Data Presentation: Bar Graph versus Histogram.
- 4. Methods of Central Tendency (Mean, Median, Mode).
- 5. Methods of Dispersion Measurement.
- 6. Range, Variance, Standard Deviation.
- 7. Quartiles, Deciles, Percentiles, Coefficient of Variation.
- 8. Five-Number Summary and Box Plot.
- Assignment- #17:
- Class #18 Statistics Part-2
- 1. Coefficient of Correlation.
- 2. Standard Scores: Z-Score, T-Score.
- 3. Normal Distribution.
- 4. Hypothesis Testing: Z-Test, T-Test.
- Assignment- #18:
- Class #19 Matplotlib for Exploratory Data Visualization
- 1. Generating Multiple Plots on a Single Canvas.
- 2. Employing Matplotlib's Object-Oriented Approach.
- 3. Crafting Inset Plots.
- 4. Generating Figures and Subplots.
- 5. Saving and Enhancing Figures.
- Assignment- #19:
- Class #20 Exploratory Data Visualization using Matplotlib & Pandas
- 1. Built-in Data Visualization in Pandas.
- 2. Utilizing Style Sheets.
- 3. Area Plot, Bar/Horizontal Bar Chart.
- 4. Histogram, Line Chart.
- 5. Scatter Plot, Box Plot.
- 6. Hexagonal Bin Plot, Pie Chart.
- 7. Kernel Density Estimation Plot (KDE).
- Assignment- #20:
- Class #21 Visualization with Seaborn
- 1. Seaborn: Distribution Plot, Lmplot.
- 2. Jointplot, Pairplot, Kdeplot.
- 3. Stripplot, Swarmplot, Boxplot.
- 4. Violinplot, Pointplot.
- 5. Axis Grids, Matrix Plot, Heatmap.
- 6. Seaborn Figure Styles.
- Assignment- #21:
- Class #22 Machine Learning Fundamentals
- 1. Introduction to Machine Learning: Definition and Importance.
- 2. Applications of Machine Learning.
- 3. Supervised Learning.
- 4. Unsupervised Learning.
- 5. Understanding Machine Learning Models.
- 6. Data Splitting: Training and Test Sets.
- 7. K-Fold Cross-Validation.
- 8. Addressing Underfitting and Overfitting.
- 9. Confusion Matrix Metrics: Precision, Recall, F1 Score.
- Assignment- #22:
- Class #23 Feature Engineering in Scikit-learn
- 1. Explore the theory behind Feature Scaling.
- 2. Get hands-on experience with Feature Scaling techniques.
- 3. Delve into Principal Component Analysis (PCA).
- 4. Practice Principal Component Analysis (PCA) with real-world examples.
- 5. Experience Label Encoding through practical exercises.
- 6. Apply Ordinal Encoding in hands-on activities.
- 7. Master One Hot Encoding with practical examples.
- 8. Learn to remove outliers through hands-on exercises.
- Assignment- #23:
- Class #24 Scikit-learn - Linear Regression Versus Multiple Regression
- 1. Linear Regression Theory.
- 2. Application of Simple Linear Regression Model.
- 3. Multiple Linear Regression Theory.
- 4. Application of Multiple Linear Regression Model.
- 5. Project 01: Overview Data Project.
- 6. Project 01: Solutions Data Project.
- Assignment- #24:
- Class #25 Scikit-learn: K Nearest Neighbors and Logistic Regression
- 1. Binary Logistic Regression Theory.
- 2. Binary Logistic Regression Algorithm.
- 3. Hands-on Binary Logistic Regression Model.
- 4. K Nearest Neighbors Theory.
- 5. K Nearest Neighbors Algorithm.
- 6. Pen & Paper Exercise for K Nearest Neighbors.
- 7. Hands-on with K Nearest Neighbors.
- 8. Project Overview: K Nearest Neighbors.
- 9. Solutions for K Nearest Neighbors Project.
- Assignment- #25:
- Class #26 Naive Bayes Classification using Scikit-learn
- 1. Saving and Loading Trained Machine Learning Models.
- 2. Implementing K-Fold Cross Validation.
- 3. Introduction to Kaggle Platform.
- 4. Introduction to Google Colab.
- 5. Naive Bayes Classification Theory.
- 6. Naive Bayes Classification Algorithm.
- 7. Pen & Paper Exercise for Naive Bayes Classification.
- 8. Hands-on with Naive Bayes Classification.
- Assignment- #26:
- Class #27 Scikit-learn: Decision Trees, Random Forests, and Ensemble
- Learning
- 1. Theory of Decision Trees: Entropy, Information Gain.
- 2. Hands-on with Decision Trees.
- 3. Introduction to Ensemble Learning: Bagging, Random Forests, Boosting.
- 4. Hands-on with Bagging.
- 5. Hands-on with Random Forests.
- Assignment- #27:
- Class #28 Scikit-learn - Support Vector Machines (SVM)
- 1. Utilizing Grid Search CV for Finding the Best Model and Hyperparameter Tuning.
- 2. Theory of Support Vector Machines.
- 3. Algorithm for Support Vector Machines.
- 4. Hands-on with Support Vector Machines (SVMs).
- 5. Project Overview: Support Vector Machines.
- 6. Solutions for Support Vector Machines Project.
- 7. Practical Uses of Natural Language Processing (NLP) Overview.
- Assignment- #28:
- Class #29 Scikit-learn - Clustering with K Means
- 1. Theory of K-Means Clustering.
- 2. Algorithm for K-Means Clustering.
- 3. Modified Algorithm for K-Means Clustering.
- 4. Pen & Paper Exercise for K-Means Clustering.
- 5. Hands-on with K-Means Clustering.
- 6. Projects Overview: K-Means Clustering.
- 7. Solutions for K-Means Clustering Project.
- Assignment- #29:
- Class #30 Natural Language Processing (NLP)
- 1. What is Natural Language Processing?
- 2. Practical Uses of Natural Language Processing (NLP).
- 3. Practical Uses of Natural Language Processing (NLP) Overview.
- Assignment- #30:
- Class #31 Deep Learning
- 1. Understanding Neurons.
- 2. Biological Neural Networks (BNNs).
- 3. Artificial Neural Networks (ANNs).
- Class #32 Python with Data Science and Machine Learning Course
- Overview & Career Path Discussion
- 1. Overview the Course.
- 2. Writing CV.
- 3. Job Searching.
This Course is Designed for
Anyone interested to learn freelancing
Job seekers
Students
Anyone interested to learn Python Machine Learning
Career Opportunities
Freelancing can be your first priority if you want to pursue an independent and flexible career. Many countries offer loads of work on platforms including Freelancer.com, Upwork, Themeforest where you can work using the skills.
Exclusive Solutions That Set Us Apart
Online Live Batch
Do you live abroad or prefer a remote learning process? We have launched online batches with all the offline facilities so that you can keep up with the technical advancement of today’s world. Now you can enroll in any course from anywhere, at any time.
Review Class
Do you face difficulty when you review the previous concepts? To ensure the best learning outcome, we arrange review classes that help our students overcome any problem in their skill development process. You will be able to understand the topics that you find complex under the close supervision of our skilled mentors.
Practice lab support
We offer our students practice lab support so that they can complete their courseworks feasibly at any time. The uninterrupted learning environment that we ensure helps the student gather practical knowledge in an efficient manner.
Class Videos
No need to worry if you miss a topic in the class. We record most of our classes so that students who miss a session can still get the information they need. They can watch the videos again and again until they understand the topic thoroughly. Our motto is to provide you a flexible learning experience to gradually improve your competence.
Career Placement Support
Our career placement department is ready to help you find a lucrative job. We ensure your resume gets into the hands of the right hiring manager. So far, this department has helped more than 16000 students to find jobs in competitive global platforms. Promising a better future, we have successfully raised the job placement rate to 66% in 2023.
Virtual Internship
Without in-hand experience, no one can be competent in any skill. Practical work experience is a must-have for better career opportunities. CIT offers its students virtual internship opportunities, where they can work under the supervision of industry experts. The online internships qualify to be as effective as offline work experience. Hence, you can also complete our internship at our office.