Python Machine learning Training

The Python Machine Learning Course at Techfliez Institute is a specialized, hands-on program designed for individuals aiming to build expertise in data science and artificial intelligence. This course offers a blend of theoretical knowledge and practical application, making it suitable for both beginners and those with a background in programming who want to dive deeper into machine learning.

COURSE CONTENT

  • Introduction to ML
  • What is ML? Why ML?
  • Introduction to Supervised ML
  • Introduction to Unsupervised ML
  • Difference Between AI|DLIML
  • Application and Use.

  • ML Libraries
  • NumPy: Introduction to NumPy
  • Pandas: Introduction| Data Frame/ Loading datasets /Loading data from database/ Pandas Operation.
  • Matplotlib: Introduction Line Chart Pie Chart| Scatter Plot /Bar chart |Histogram
  • Sklearn:: Introduction|Sklearn-API|
  • Statsmodels.API.
  • ML Glossary
  • Variable types, k-fold CV, AUC, F1 score, Overfitting/Underfitting,
  • Generalization, ROC|Confusion Matrix
  • Mathematical Background for ML- Matrix ops Probability Theory (Bayes’ Theorem)
  • Statistical Knowledge for ML- Mean, Median, Mode, Z-Scores, Bias -Variance Dichotomy Exploratory Data Analysis Using Visualisation Scikit-Learn Library for ML Code Exercises

  • Data Collection. The Quantity & Quality of your data dictate how accurate our model is….
  • Data Preparation. Wrangle data and prepare it for Training Data Wrangling using Pandas | Pre-processing
  • Data and Feature Engineering | Data Split
  • Choose a Model.
  • Train the Model….
  • Evaluate the Model….
  • 6- Parameter Tuning | hyper parameter training
  • Make Predictions.

  • Introduction/Maths behind Supervised Machine Learning and Algo.

  • Linear Regression
  • Multi-Linear Regression
  • Lasso/Rigde
  • Decision Tree Regressor
  • Support Vector Regressor

  • Logistic Regression
  • KNN-K Nearest Neighbours
  • Support Vector Classifier (SVM-SVC)
  • Decision Tree Classifier (DTC)
  • Random Forest
  • Naïve Bayes
  • Ensemble Learning

  • Introduction: Mathematics behind Clustering

  • Implementation of K-mean Clustering

    Implementation of H-clustering
  • Code Exercises

  • Apiori rule

  • Principle Component Analysis (PCA).

  • IBM ATTRITION RATE PREDICTION USING MACHINE LEARNING
  • COVD-19 PATIENT OUTCOME PREDICTION USING ML
  • ESTIMATE THE ONLINE SALES OF ANY E-COMMERCE RETAIL FIRM USING ML
  • GUI BASED MACHINE LEARNING APPLICATION TO CLASSIFY THE PLANT SPECIES OF IRIS FLOWER
  • PREDICT THE CHURN RATE IN A TELECOM COMPANY USING ML
  • MALL CUSTOMER SEGMENTATION USING ML
  • MARKET BASKET ANALYSIS AND ASSIT A SHOPPING MALL TO STACK PRODUCT
  • PREDICT AND ESTIMATE CAR RE-SALE VALUE USING MACHINE LEARNING
  • WORKING ON INBULIT DATASETS
  • PREDICTION! CLASSIFICATION OF HANDWRITTEN DIGITS

TRAINING FEATURES

Foundation in Python for Data Science

Students start with an introduction to Python programming, focusing on essential libraries and data manipulation techniques. Topics include data structures, functions, and libraries like Pandas and NumPy, which are crucial for handling data in machine learning.

In-Depth Advanced Java Modules

Includes advanced topics such as JDBC, Servlets, JSP, and web services, with a focus on building scalable applications using Java Enterprise Edition (Java EE).

Comprehensive Machine Learning Algorithms

The course covers a range of algorithms, including linear and logistic regression, decision trees, random forests, support vector machines, clustering algorithms, and neural networks, providing students with a robust toolkit to tackle various data science challenges.

Data Preparation and Feature Engineering

Prepares students to handle real-world data, including techniques for data cleaning, transformation, feature scaling, and handling missing values, along with advanced feature engineering for optimal model performance.

Exploratory Data Analysis (EDA)

EDA techniques are covered extensively, teaching students to visualize data, discover patterns, and generate insights. Using tools like Matplotlib and Seaborn, students learn to create impactful data visualizations and explore data distributions, correlations, and trends.

Practical, Real-World Projects

Throughout the course, students work on a series of projects involving tasks like predicting trends, classifying data, and clustering. This project-based approach enables learners to apply machine learning concepts in realistic contexts and build a portfolio of work to showcase their skills.

Deep Learning Introduction

Introduces students to deep learning and neural networks using TensorFlow and Keras. This section covers the basics of building and training neural networks, enabling students to explore applications in computer vision, natural language processing, and more.

Personalized Learning and Support

With experienced instructors, personalized feedback, and mentorship, students receive guidance tailored to their learning pace and career goals, enhancing their understanding of complex topics and problem-solving skills.

Certification and Career Guidance

Upon completion, students earn a Techfliez Institute certificate in Python Machine Learning, validating their skills for employers. The course includes career support with interview preparation, resume building, and networking advice to help students transition into roles in data science, machine learning, or AI.